Machine Learning : The Quality of Life

The project aims to identify various infrastructures such as schools, hospitals, historical buildings, pharmacies, universities, cinemas, festival, and more abstract aspects like crime rates and unemployment rate, to ultimately assign a quality-of-life score for each municipality. The goal originally was to train a machine learning model to estimate whether a city, based on its buildings and other criteria, can be expected to provide a good quality of life for its residents.

Stage 1 : the goal was to try as much models as possible in order to get a good look at the correlation between our datasets. Our target was the column "MED14", which qualifies the quality of life.

Stage 2 : After a thorough analysis, regarding the quality of data and our results, we chose to predict the number of properties per inhabitants, the average price of housing per square meter and population growth. We are doing this by changing our target column compared to what we wanted to do in stage 1.

import pandas as pd
from scipy.stats import beta
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error, r2_score

Nettoyage

The first thing we did was cleaning our different datasets. We removed columns that are not necessary for our study from each dataset. And we treated the missing data for certain datasets. Finally, we saved a new clean csv file for each dataset.

Batiment historique

df1 = pd.read_csv("zone batiment historique 2015.csv", sep=",", encoding="ISO-8859-1")
df1.head()

<ipython-input-43-621e85e581a9>:1: DtypeWarning: Columns (0,4,6,61,62,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,237,238,241,242,243,244,245,246,247,248,249,250,251,252,253,255) have mixed types. Specify dtype option on import or set low_memory=False.
  df1 = pd.read_csv("zone batiment historique 2015.csv", sep=",", encoding="ISO-8859-1")

	N°	REFERENCE	étude	région	département	Commune	INSEE	Adresse	dénomination	titre courant	...	Unnamed: 246	Unnamed: 247	Unnamed: 248	Unnamed: 249	Unnamed: 250	Unnamed: 251	Unnamed: 252	Unnamed: 253	Unnamed: 254	Unnamed: 255
0	1	PA00132969	recensement immeubles MH	Rhône-Alpes	1	'Abergement-Clémenciat (L'')'	1001	NaN	site archéologique	Site médiéval du Vieux Bourg	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2	PA00116287	recensement immeubles MH	Rhône-Alpes	1	Amareins-Francheleins-Cesseins	1165	NaN	église	Eglise	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	3	PA00116291	recensement immeubles MH	Rhône-Alpes	1	Ambronay	1007	Thollon (place)	abbaye	Ancienne abbaye d’Ambronay	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	4	PA00125733	recensement immeubles MH	Rhône-Alpes	1	Ambronay	1007	NaN	fort	site archéologique	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	5	PA00116288	recensement immeubles MH	Rhône-Alpes	1	Ambérieu-en-Bugey	1004	NaN	château	site archéologique	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 256 columns

df12 = df1.drop(['affectataire', 'protection', 'préc. Protection',
       'date protection', 'auteur(s)', 'siècle', 'Adresse','N°', 'REFERENCE', 'étude', 'région', 'département','Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',
       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',
       'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',
       'Unnamed: 28', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31',
       'Unnamed: 32', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35',
       'Unnamed: 36', 'Unnamed: 37', 'Unnamed: 38', 'Unnamed: 39',
       'Unnamed: 40', 'Unnamed: 41', 'Unnamed: 42', 'Unnamed: 43',
       'Unnamed: 44', 'Unnamed: 45', 'Unnamed: 46', 'Unnamed: 47',
       'Unnamed: 48', 'Unnamed: 49', 'Unnamed: 50', 'Unnamed: 51',
       'Unnamed: 52', 'Unnamed: 53', 'Unnamed: 54', 'Unnamed: 55',
       'Unnamed: 56', 'Unnamed: 57', 'Unnamed: 58', 'Unnamed: 59',
       'Unnamed: 60', 'Unnamed: 61', 'Unnamed: 62', 'Unnamed: 63',
       'Unnamed: 64', 'Unnamed: 65', 'Unnamed: 66', 'Unnamed: 67',
       'Unnamed: 68', 'Unnamed: 69', 'Unnamed: 70', 'Unnamed: 71',
       'Unnamed: 72', 'Unnamed: 73', 'Unnamed: 74', 'Unnamed: 75',
       'Unnamed: 76', 'Unnamed: 77', 'Unnamed: 78', 'Unnamed: 79',
       'Unnamed: 80', 'Unnamed: 81', 'Unnamed: 82', 'Unnamed: 83',
       'Unnamed: 84', 'Unnamed: 85', 'Unnamed: 86', 'Unnamed: 87',
       'Unnamed: 88', 'Unnamed: 89', 'Unnamed: 90', 'Unnamed: 91',
       'Unnamed: 92', 'Unnamed: 93', 'Unnamed: 94', 'Unnamed: 95','Unnamed: 96', 'Unnamed: 97', 'Unnamed: 98', 'Unnamed: 99',
       'Unnamed: 100', 'Unnamed: 101', 'Unnamed: 102', 'Unnamed: 103',
       'Unnamed: 104', 'Unnamed: 105','Unnamed: 106', 'Unnamed: 107', 'Unnamed: 108', 'Unnamed: 109',
       'Unnamed: 110', 'Unnamed: 111', 'Unnamed: 112', 'Unnamed: 113',
       'Unnamed: 114', 'Unnamed: 115','Unnamed: 116', 'Unnamed: 117', 'Unnamed: 118', 'Unnamed: 119',
       'Unnamed: 120', 'Unnamed: 121', 'Unnamed: 122', 'Unnamed: 123',
       'Unnamed: 124', 'Unnamed: 125','Unnamed: 126', 'Unnamed: 127', 'Unnamed: 128', 'Unnamed: 129',
       'Unnamed: 130', 'Unnamed: 131', 'Unnamed: 132', 'Unnamed: 133',
       'Unnamed: 134', 'Unnamed: 135','Unnamed: 136', 'Unnamed: 137', 'Unnamed: 138', 'Unnamed: 139',
       'Unnamed: 140', 'Unnamed: 141', 'Unnamed: 142', 'Unnamed: 143',
       'Unnamed: 144', 'Unnamed: 145','Unnamed: 146', 'Unnamed: 147', 'Unnamed: 148', 'Unnamed: 149',
       'Unnamed: 150', 'Unnamed: 151', 'Unnamed: 152', 'Unnamed: 153',
       'Unnamed: 154', 'Unnamed: 155','Unnamed: 156', 'Unnamed: 157', 'Unnamed: 158', 'Unnamed: 159',
       'Unnamed: 160', 'Unnamed: 161', 'Unnamed: 162', 'Unnamed: 163',
       'Unnamed: 164', 'Unnamed: 165','Unnamed: 166', 'Unnamed: 167', 'Unnamed: 168', 'Unnamed: 169',
       'Unnamed: 170', 'Unnamed: 171', 'Unnamed: 172', 'Unnamed: 173',
       'Unnamed: 174', 'Unnamed: 175','Unnamed: 176', 'Unnamed: 177', 'Unnamed: 178', 'Unnamed: 179',
       'Unnamed: 180', 'Unnamed: 181', 'Unnamed: 182', 'Unnamed: 183',
       'Unnamed: 184', 'Unnamed: 185','Unnamed: 186', 'Unnamed: 187', 'Unnamed: 188', 'Unnamed: 189',
       'Unnamed: 190', 'Unnamed: 191', 'Unnamed: 192', 'Unnamed: 193',
       'Unnamed: 194', 'Unnamed: 195','Unnamed: 196', 'Unnamed: 197', 'Unnamed: 198', 'Unnamed: 199',
       'Unnamed: 200', 'Unnamed: 201', 'Unnamed: 202', 'Unnamed: 203',
       'Unnamed: 204', 'Unnamed: 205','Unnamed: 206', 'Unnamed: 207', 'Unnamed: 208', 'Unnamed: 209',
       'Unnamed: 210', 'Unnamed: 211', 'Unnamed: 212', 'Unnamed: 213',
       'Unnamed: 214', 'Unnamed: 215','Unnamed: 216', 'Unnamed: 217', 'Unnamed: 218', 'Unnamed: 219',
       'Unnamed: 220', 'Unnamed: 221', 'Unnamed: 222', 'Unnamed: 223',
       'Unnamed: 224', 'Unnamed: 225','Unnamed: 226', 'Unnamed: 227', 'Unnamed: 228', 'Unnamed: 229',
       'Unnamed: 230', 'Unnamed: 231', 'Unnamed: 232', 'Unnamed: 233',
       'Unnamed: 234', 'Unnamed: 235','Unnamed: 236', 'Unnamed: 237', 'Unnamed: 238', 'Unnamed: 239',
       'Unnamed: 240', 'Unnamed: 241', 'Unnamed: 242', 'Unnamed: 243',
       'Unnamed: 244', 'Unnamed: 245','Unnamed: 246', 'Unnamed: 247', 'Unnamed: 248', 'Unnamed: 249',
       'Unnamed: 250', 'Unnamed: 251', 'Unnamed: 252', 'Unnamed: 253',
       'Unnamed: 254', 'Unnamed: 255'], axis=1)

df12.to_csv('batiment_historique_finale.csv')

Crime et délit

df3 = pd.read_parquet("donnee-comm-data.gouv-parquet-2023-geographie2024-produit-le2024-07-05.parquet")
df3.head()

	CODGEO_2024	annee	classe	unité.de.compte	valeur.publiée	faits	tauxpourmille	complementinfoval	complementinfotaux	POP	millPOP	LOG	millLOG
0	01001	16	Coups et blessures volontaires	victime	ndiff	NaN	NaN	1.698925	1.498563	767	16	348.0	16
1	01001	16	Coups et blessures volontaires intrafamiliaux	victime	diff	0.0	0.0	NaN	NaN	767	16	348.0	16
2	01001	16	Autres coups et blessures volontaires	victime	ndiff	NaN	NaN	1.511628	0.926155	767	16	348.0	16
3	01001	16	Violences sexuelles	victime	diff	0.0	0.0	NaN	NaN	767	16	348.0	16
4	01001	16	Vols avec armes	infraction	diff	0.0	0.0	NaN	NaN	767	16	348.0	16

df32 = df3.drop(['annee','valeur.publiée',
       'faits', 'tauxpourmille', 'complementinfoval', 'complementinfotaux',
       'POP', 'millPOP', 'LOG', 'millLOG'], axis = 1)
df32.to_csv('crime_delit.csv')

Hopitaux

df5 = pd.read_csv("fichier_hopitaux.csv")
df5.head()

<ipython-input-47-dcd464753eed>:1: DtypeWarning: Columns (18,26,28,71) have mixed types. Specify dtype option on import or set low_memory=False.
  df5 = pd.read_csv("fichier_hopitaux.csv")

	siren	nic	siret	statutDiffusionEtablissement	dateCreationEtablissement	trancheEffectifsEtablissement	anneeEffectifsEtablissement	activitePrincipaleRegistreMetiersEtablissement	dateDernierTraitementEtablissement	etablissementSiege	...	codePaysEtranger2Etablissement	libellePaysEtranger2Etablissement	etatAdministratifEtablissement	enseigne1Etablissement	enseigne2Etablissement	enseigne3Etablissement	denominationUsuelleEtablissement	activitePrincipaleEtablissement	nomenclatureActivitePrincipaleEtablissement	caractereEmployeurEtablissement
0	5720164	28	572016400028	O	1983-09-28	22	2019.0	NaN	2021-10-27T08:00:29	True	...	NaN	NaN	A	NaN	NaN	NaN	NaN	86.10Z	NAFRev2	O
1	7050040	18	705004000018	O	1970-01-01	22	2019.0	NaN	2021-10-27T08:00:30	True	...	NaN	NaN	A	NaN	NaN	NaN	NaN	86.10Z	NAFRev2	O
2	17150533	10	1715053300010	O	1971-01-01	22	2019.0	NaN	2021-10-27T08:00:38	True	...	NaN	NaN	A	NaN	NaN	NaN	NaN	86.10Z	NAFRev2	O
3	36220150	27	3622015000027	O	1997-01-27	21	2019.0	NaN	2022-04-14T04:02:36	True	...	NaN	NaN	A	NaN	NaN	NaN	POLE ANTIBES SAINT JEAN	86.10Z	NAFRev2	O
4	46620241	11	4662024100011	O	1966-01-01	12	2019.0	NaN	2022-05-19T04:02:10	True	...	NaN	NaN	A	NaN	NaN	NaN	NaN	86.10Z	NAFRev2	O

5 rows × 76 columns

df52 = df5.drop(['siren', 'nic', 'siret', 'statutDiffusionEtablissement',
       'dateCreationEtablissement', 'trancheEffectifsEtablissement',
       'anneeEffectifsEtablissement',
       'activitePrincipaleRegistreMetiersEtablissement',
       'dateDernierTraitementEtablissement', 'etablissementSiege','etatAdministratifUniteLegale', 'statutDiffusionUniteLegale',
       'unitePurgeeUniteLegale', 'dateCreationUniteLegale',
       'categorieJuridiqueUniteLegale',       'sigleUniteLegale', 'denominationUsuelle1UniteLegale',
       'denominationUsuelle2UniteLegale', 'denominationUsuelle3UniteLegale',
       'sexeUniteLegale', 'nomUniteLegale', 'nomUsageUniteLegale',
       'prenom1UniteLegale', 'prenom2UniteLegale', 'prenom3UniteLegale',
       'prenom4UniteLegale', 'prenomUsuelUniteLegale', 'pseudonymeUniteLegale','activitePrincipaleUniteLegale',
       'nomenclatureActivitePrincipaleUniteLegale',
       'identifiantAssociationUniteLegale',
       'economieSocialeSolidaireUniteLegale', 'caractereEmployeurUniteLegale',
       'trancheEffectifsUniteLegale', 'anneeEffectifsUniteLegale',
       'nicSiegeUniteLegale', 'dateDernierTraitementUniteLegale', 'categorieEntreprise', 'anneeCategorieEntreprise',
       'complementAdresseEtablissement', 'numeroVoieEtablissement',
       'indiceRepetitionEtablissement', 'typeVoieEtablissement',
       'libelleVoieEtablissement', 'libelleCommuneEtrangerEtablissement',
       'distributionSpecialeEtablissement','codeCedexEtablissement', 'libelleCedexEtablissement',
       'codePaysEtrangerEtablissement', 'libellePaysEtrangerEtablissement',
       'complementAdresse2Etablissement', 'numeroVoie2Etablissement',
       'indiceRepetition2Etablissement', 'typeVoie2Etablissement',
       'libelleVoie2Etablissement', 'codePostal2Etablissement',
       'libelleCommune2Etablissement', 'libelleCommuneEtranger2Etablissement',
       'distributionSpeciale2Etablissement', 'codeCommune2Etablissement',
       'codeCedex2Etablissement', 'libelleCedex2Etablissement',
       'codePaysEtranger2Etablissement', 'libellePaysEtranger2Etablissement',
       'etatAdministratifEtablissement', 'enseigne1Etablissement',
       'enseigne2Etablissement', 'enseigne3Etablissement',
       'denominationUsuelleEtablissement', 'activitePrincipaleEtablissement',
       'nomenclatureActivitePrincipaleEtablissement',
       'caractereEmployeurEtablissement' ], axis=1)

df52.to_csv('hopitaux.csv')

Ecole

df6 = pd.read_csv("fr-en-adresse-et-geolocalisation-etablissements-premier-et-second-degre.csv", sep=';', encoding='utf-8')
df6.head()

<ipython-input-49-f92bd4eeea22>:1: DtypeWarning: Columns (22,25) have mixed types. Specify dtype option on import or set low_memory=False.
  df6 = pd.read_csv("fr-en-adresse-et-geolocalisation-etablissements-premier-et-second-degre.csv", sep=';', encoding='utf-8')

	numero_uai	appellation_officielle	denomination_principale	patronyme_uai	secteur_public_prive_libe	adresse_uai	lieu_dit_uai	boite_postale_uai	code_postal_uai	localite_acheminement_uai	...	code_commune	libelle_departement	libelle_region	libelle_academie	position	secteur_prive_code_type_contrat	secteur_prive_libelle_type_contrat	code_ministere	libelle_ministere	date_ouverture
0	0280132S	Ecole primaire Jean Lelievre	ECOLE PRIMAIRE PUBLIQUE	JEAN LELIEVRE	Public	1 rue de l'Eglise	NaN	NaN	28500	MARVILLE MOUTIERS BRULE	...	28239	Eure-et-Loir	Centre-Val de Loire	Orléans-Tours	48.675859467641374, 1.3915955785995753	99.0	SANS OBJET	6	MINISTERE DE L'EDUCATION NATIONALE	1967-02-08
1	0280140A	Ecole primaire	ECOLE PRIMAIRE	TREON	Public	4 rue Lucien Rialet	NaN	NaN	28500	TREON	...	28394	Eure-et-Loir	Centre-Val de Loire	Orléans-Tours	48.677196333593336, 1.3264305550807276	99.0	SANS OBJET	6	MINISTERE DE L'EDUCATION NATIONALE	1967-02-08
2	0280146G	Ecole élémentaire Louis Pergaud	ECOLE ELEMENTAIRE PUBLIQUE	LOUIS PERGAUD	Public	1 rue de l'Epinay	NaN	NaN	28500	VERNOUILLET	...	28404	Eure-et-Loir	Centre-Val de Loire	Orléans-Tours	48.725453828248966, 1.3319293827210517	99.0	SANS OBJET	6	MINISTERE DE L'EDUCATION NATIONALE	1967-02-08
3	0280155S	Ecole primaire	ECOLE PRIMAIRE PUBLIQUE	BOISVILLE-LA-SAINT-PERE-ALLONES	Public	1 rue du Stade	NaN	NaN	28150	BOISVILLE LA ST PERE	...	28047	Eure-et-Loir	Centre-Val de Loire	Orléans-Tours	48.32728348160609, 1.6946294598047125	99.0	SANS OBJET	6	MINISTERE DE L'EDUCATION NATIONALE	1967-02-07
4	0280162Z	Ecole primaire	ECOLE PRIMAIRE PUBLIQUE	NaN	Public	3 rue de la République	NaN	NaN	28150	OUARVILLE	...	28291	Eure-et-Loir	Centre-Val de Loire	Orléans-Tours	48.35397582025789, 1.7785743335767488	99.0	SANS OBJET	6	MINISTERE DE L'EDUCATION NATIONALE	1967-02-07

5 rows × 35 columns

df62 = df6.drop(['numero_uai','lieu_dit_uai','boite_postale_uai','secteur_public_prive_libe','etat_etablissement','coordonnee_x',
       'coordonnee_y', 'EPSG', 'latitude', 'longitude', 'appariement',
       'localisation', 'nature_uai','etat_etablissement_libe', 'code_departement', 'code_region',
       'code_academie','libelle_departement',
       'libelle_region', 'libelle_academie','position',
       'secteur_prive_code_type_contrat', 'secteur_prive_libelle_type_contrat',
       'code_ministere', 'libelle_ministere', 'date_ouverture' ], axis=1)

df62.to_csv('école_premier_seconde_deg.csv')

Population

df_pop = pd.read_csv("base-cc-evol-struct-pop-2021.CSV", sep = ";")
df_pop.head()

<ipython-input-51-4528f3a46e66>:1: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  df_pop = pd.read_csv("base-cc-evol-struct-pop-2021.CSV", sep = ";")

	CODGEO	P21_POP	P21_POP0014	P21_POP1529	P21_POP3044	P21_POP4559	P21_POP6074	P21_POP7589	P21_POP90P	P21_POPH	...	C10_POP2554_CS8	C10_POP55P	C10_POP55P_CS1	C10_POP55P_CS2	C10_POP55P_CS3	C10_POP55P_CS4	C10_POP55P_CS5	C10_POP55P_CS6	C10_POP55P_CS7	C10_POP55P_CS8
0	1001	832.0	158.552045	100.025669	152.787222	198.071134	149.720218	67.098389	5.745324	423.485017	...	0.000000	225.148718	0.0	0.000000	16.082051	4.020513	20.102564	12.061538	164.841026	8.041026
1	1002	267.0	57.046286	32.439093	57.272224	58.101506	40.504740	19.768434	1.867717	139.890359	...	7.555556	68.000000	0.0	0.000000	0.000000	0.000000	0.000000	7.555556	60.444444	0.000000
2	1004	14854.0	2789.758194	3081.375266	2740.192625	2632.565876	2269.033228	1152.137702	188.937109	7125.780104	...	538.066904	3477.141561	0.0	65.560548	69.417225	137.972930	136.692417	136.554034	2673.140809	257.803598
3	1005	1897.0	367.334805	289.213773	388.963943	415.518703	301.217072	123.672440	11.079265	950.047549	...	28.000000	392.000000	0.0	8.000000	8.000000	8.000000	8.000000	12.000000	316.000000	32.000000
4	1006	113.0	9.912281	13.877193	19.824561	23.789474	37.666667	7.929825	0.000000	59.473684	...	8.285714	53.857143	0.0	0.000000	0.000000	8.285714	8.285714	0.000000	37.285714	0.000000

5 rows × 292 columns

df_pop2 = df_pop.drop(['P21_POPH',
       'P21_H0014', 'P21_H1529', 'P21_H3044', 'P21_H4559', 'P21_H6074',
       'P21_H7589', 'P21_H90P', 'P21_H0019', 'P21_H2064', 'P21_H65P',
       'P21_POPF', 'P21_F0014', 'P21_F1529', 'P21_F3044', 'P21_F4559',
       'P21_F6074', 'P21_F7589', 'P21_F90P', 'P21_F0019', 'P21_F2064',
       'P21_F65P', 'P21_POP01P', 'P21_POP01P_IRAN1', 'P21_POP01P_IRAN2',
       'P21_POP01P_IRAN3', 'P21_POP01P_IRAN4', 'P21_POP01P_IRAN5',
       'P21_POP01P_IRAN6', 'P21_POP01P_IRAN7', 'P21_POP0114_IRAN2P',
       'P21_POP0114_IRAN2', 'P21_POP0114_IRAN3P', 'P21_POP1524_IRAN2P',
       'P21_POP1524_IRAN2', 'P21_POP1524_IRAN3P', 'P21_POP2554_IRAN2P',
       'P21_POP2554_IRAN2', 'P21_POP2554_IRAN3P', 'P21_POP55P_IRAN2P',
       'P21_POP55P_IRAN2', 'P21_POP55P_IRAN3P', 'C21_POP15P', 'C21_POP15P_CS1',
       'C21_POP15P_CS2', 'C21_POP15P_CS3', 'C21_POP15P_CS4', 'C21_POP15P_CS5',
       'C21_POP15P_CS6', 'C21_POP15P_CS7', 'C21_POP15P_CS8', 'C21_H15P',
       'C21_H15P_CS1', 'C21_H15P_CS2', 'C21_H15P_CS3', 'C21_H15P_CS4',
       'C21_H15P_CS5', 'C21_H15P_CS6', 'C21_H15P_CS7', 'C21_H15P_CS8',
       'C21_F15P', 'C21_F15P_CS1', 'C21_F15P_CS2', 'C21_F15P_CS3',
       'C21_F15P_CS4', 'C21_F15P_CS5', 'C21_F15P_CS6', 'C21_F15P_CS7',
       'C21_F15P_CS8', 'C21_POP1524', 'C21_POP1524_CS1', 'C21_POP1524_CS2',
       'C21_POP1524_CS3', 'C21_POP1524_CS4', 'C21_POP1524_CS5',
       'C21_POP1524_CS6', 'C21_POP1524_CS7', 'C21_POP1524_CS8', 'C21_POP2554',
       'C21_POP2554_CS1', 'C21_POP2554_CS2', 'C21_POP2554_CS3',
       'C21_POP2554_CS4','C21_POP2554_CS5', 'C21_POP2554_CS6', 'C21_POP2554_CS7',
       'C21_POP2554_CS8', 'C21_POP55P', 'C21_POP55P_CS1', 'C21_POP55P_CS2',
       'C21_POP55P_CS3', 'C21_POP55P_CS4', 'C21_POP55P_CS5','C21_POP55P_CS6', 'C21_POP55P_CS7', 'C21_POP55P_CS8', 'P15_POP',
       'P15_POP0014', 'P15_POP1529', 'P15_POP3044', 'P15_POP4559',
       'P15_POP6074', 'P15_POP7589','P15_POP90P', 'P15_POPH', 'P15_H0014', 'P15_H1529', 'P15_H3044',
       'P15_H4559', 'P15_H6074', 'P15_H7589', 'P15_H90P', 'P15_H0019','P15_H2064', 'P15_H65P', 'P15_POPF', 'P15_F0014', 'P15_F1529',
       'P15_F3044', 'P15_F4559', 'P15_F6074', 'P15_F7589', 'P15_F90P','P15_F0019', 'P15_F2064', 'P15_F65P', 'P15_POP01P', 'P15_POP01P_IRAN1',
       'P15_POP01P_IRAN2', 'P15_POP01P_IRAN3', 'P15_POP01P_IRAN4',
       'P15_POP01P_IRAN5', 'P15_POP01P_IRAN6','P15_POP01P_IRAN7', 'P15_POP0114_IRAN2P', 'P15_POP0114_IRAN2',
       'P15_POP0114_IRAN3P', 'P15_POP1524_IRAN2P', 'P15_POP1524_IRAN2',
       'P15_POP1524_IRAN3P', 'P15_POP2554_IRAN2P', 'P15_POP2554_IRAN2',
       'P15_POP2554_IRAN3P','P15_POP55P_IRAN2P', 'P15_POP55P_IRAN2', 'P15_POP55P_IRAN3P',
       'C15_POP15P', 'C15_POP15P_CS1', 'C15_POP15P_CS2', 'C15_POP15P_CS3',
       'C15_POP15P_CS4', 'C15_POP15P_CS5', 'C15_POP15P_CS6','C15_POP15P_CS7', 'C15_POP15P_CS8', 'C15_H15P', 'C15_H15P_CS1',
       'C15_H15P_CS2', 'C15_H15P_CS3', 'C15_H15P_CS4', 'C15_H15P_CS5',
       'C15_H15P_CS6', 'C15_H15P_CS7','C15_H15P_CS8', 'C15_F15P', 'C15_F15P_CS1', 'C15_F15P_CS2',
       'C15_F15P_CS3', 'C15_F15P_CS4', 'C15_F15P_CS5', 'C15_F15P_CS6',
       'C15_F15P_CS7', 'C15_F15P_CS8','C15_POP1524', 'C15_POP1524_CS1', 'C15_POP1524_CS2', 'C15_POP1524_CS3',
       'C15_POP1524_CS4', 'C15_POP1524_CS5', 'C15_POP1524_CS6',
       'C15_POP1524_CS7', 'C15_POP1524_CS8', 'C15_POP2554','C15_POP2554_CS1', 'C15_POP2554_CS2', 'C15_POP2554_CS3',
       'C15_POP2554_CS4', 'C15_POP2554_CS5', 'C15_POP2554_CS6',
       'C15_POP2554_CS7', 'C15_POP2554_CS8', 'C15_POP55P', 'C15_POP55P_CS1','C15_POP55P_CS2', 'C15_POP55P_CS3', 'C15_POP55P_CS4', 'C15_POP55P_CS5',
       'C15_POP55P_CS6', 'C15_POP55P_CS7', 'C15_POP55P_CS8', 'P10_POP',
       'P10_POP0014', 'P10_POP1529','P10_POP3044', 'P10_POP4559', 'P10_POP6074', 'P10_POP75P', 'P10_POPH',
       'P10_H0014', 'P10_H1529', 'P10_H3044', 'P10_H4559', 'P10_H6074','P10_H7589', 'P10_H90P', 'P10_H0019', 'P10_H2064', 'P10_H65P',
       'P10_POPF', 'P10_F0014', 'P10_F1529', 'P10_F3044', 'P10_F4559','P10_F6074', 'P10_F7589', 'P10_F90P', 'P10_F0019', 'P10_F2064',
       'P10_F65P', 'C10_POP15P', 'C10_POP15P_CS1', 'C10_POP15P_CS2',
       'C10_POP15P_CS3','C10_POP15P_CS4', 'C10_POP15P_CS5', 'C10_POP15P_CS6', 'C10_POP15P_CS7',
       'C10_POP15P_CS8', 'C10_H15P', 'C10_H15P_CS1', 'C10_H15P_CS2',
       'C10_H15P_CS3', 'C10_H15P_CS4','C10_H15P_CS5', 'C10_H15P_CS6', 'C10_H15P_CS7', 'C10_H15P_CS8',
       'C10_F15P', 'C10_F15P_CS1', 'C10_F15P_CS2', 'C10_F15P_CS3',
       'C10_F15P_CS4', 'C10_F15P_CS5','C10_F15P_CS6', 'C10_F15P_CS7', 'C10_F15P_CS8', 'C10_POP1524',
       'C10_POP1524_CS1', 'C10_POP1524_CS2', 'C10_POP1524_CS3',
       'C10_POP1524_CS4', 'C10_POP1524_CS5', 'C10_POP1524_CS6','C10_POP1524_CS7', 'C10_POP1524_CS8', 'C10_POP2554', 'C10_POP2554_CS1',
       'C10_POP2554_CS2', 'C10_POP2554_CS3', 'C10_POP2554_CS4',
       'C10_POP2554_CS5', 'C10_POP2554_CS6', 'C10_POP2554_CS7','C10_POP2554_CS8', 'C10_POP55P', 'C10_POP55P_CS1', 'C10_POP55P_CS2',
       'C10_POP55P_CS3', 'C10_POP55P_CS4', 'C10_POP55P_CS5', 'C10_POP55P_CS6',
       'C10_POP55P_CS7', 'C10_POP55P_CS8'], axis = 1)

#P21_POP : population en 2021
#P21_POP0014 : Nombre de personnes de 0 à 14 ans en 2021
#P21_POP1529 : Nombre de personnes de 15 à 29 ans en 2021
#P21_POP3044 : Nombre de personnes de 30 à 44 ans en 2021
#P21_POP4559 : Nombre de personnes de 45 à 59 ans en 2021
#P21_POP6074 : Nombre de personnes de 60 à 74 ans en 2021
#P21_POP7589 : Nombre de personnes de 75 à 89 ans en 2021
#P21_POP90P : Nombre de personnes de 90 ans ou plus en 2021

df_pop2

	CODGEO	P21_POP	P21_POP0014	P21_POP1529	P21_POP3044	P21_POP4559	P21_POP6074	P21_POP7589	P21_POP90P
0	1001	832.0	158.552045	100.025669	152.787222	198.071134	149.720218	67.098389	5.745324
1	1002	267.0	57.046286	32.439093	57.272224	58.101506	40.504740	19.768434	1.867717
2	1004	14854.0	2789.758194	3081.375266	2740.192625	2632.565876	2269.033228	1152.137702	188.937109
3	1005	1897.0	367.334805	289.213773	388.963943	415.518703	301.217072	123.672440	11.079265
4	1006	113.0	9.912281	13.877193	19.824561	23.789474	37.666667	7.929825	0.000000
...	...	...	...	...	...	...	...	...	...
34958	97420	24293.0	5564.329899	4853.335481	4556.391863	5370.832301	2864.635451	997.155641	86.319364
34959	97421	7243.0	1700.208360	1326.662599	1298.668993	1620.251575	896.342643	371.422147	29.443683
34960	97422	81943.0	18285.741483	16318.584869	15214.365223	16589.370571	11142.902995	4009.875265	382.159595
34961	97423	6899.0	1342.305384	1253.046383	1257.043099	1694.133409	950.602949	352.302838	49.565938
34962	97424	5390.0	1082.145083	844.494729	969.707026	1314.192831	832.305572	319.769028	27.385731

34963 rows × 9 columns

df_pop2.to_csv('population par commune.csv')

Activité économique

df_eco = pd.read_excel('MDB-INSEE-V2.xls')
df_eco = df_eco.drop(['Orientation Economique','SEG Croissance POP','LIBGEO','Dynamique Démographique BV','Environnement Démographique', 'Fidélité', 'SYN MEDICAL',
       'Seg Cap Fiscale', 'Seg Dyn Entre', 'DYN SetC', 'CP',"Urbanité Ruralité", 'REG', 'DEP','Population', 'Nb Femme', 'Nb Homme', 'Nb Mineurs', 'Nb Majeurs',
       'Nb Etudiants',
       'Moyenne Revenus Fiscaux Départementaux',
       'Moyenne Revenus Fiscaux Régionaux',
       'Score VA Région', 'Score PIB',
       'PIB Régionnal', 'SEG Environnement Démographique Obsolète',
       'Capacité Fisc', 'Moyenne Revnus fiscaux', 'Capacité Camping',
       'Taux étudiants', 'Capacité Hotel', 'Nb Atifs', 'Nb Actifs Salariés',
       'Nb Actifs Non Salariés',
       'Dep Moyenne Salaires Horaires', 'Dep Moyenne Salaires Cadre Horaires',
       'Dep Moyenne Salaires Prof Intermédiaire Horaires',
       'Dep Moyenne Salaires Employé Horaires',
       'Dep Moyenne Salaires Ouvrié Horaires', 'Reg Moyenne Salaires Horaires',
       'Reg Moyenne Salaires Cadre Horaires',
       'Reg Moyenne Salaires Prof Intermédiaire Horaires',
       'Reg Moyenne Salaires Employé Horaires',
       'Reg Moyenne Salaires Ouvrié Horaires', 'Valeur ajoutée régionale', 'Evolution Population', 'Score équipement de santé BV',
        'Score Démographique',  'Score Ménages', 'Indice Fiscal Partiel', 'Score Fiscal',
       'Indice Evasion Client', 'Score Evasion Client',
       'Score Synergie Médicale', 'Nb Pharmacies et parfumerie',
       'Nb Occupants Résidence Principale', 'Score Urbanité',
       'Taux Evasion Client',
       'Nb Industries des biens intermédiaires'], axis = 1)
df_eco.to_csv('activité economique par commune.csv')

WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero

df_eco

	CODGEO	Dynamique Entrepreneuriale	Dynamique Entrepreneuriale Service et Commerce	Synergie Médicale COMMUNE	Indice Synergie Médicale	Nb Omnipraticiens BV	Nb Infirmiers Libéraux BV	Nb dentistes Libéraux BV	Nb pharmaciens Libéraux BV	Densité Médicale BV	...	Dynamique Démographique INSEE	Capacité Fiscale	Nb Education, santé, action sociale	Nb Services personnels et domestiques	Nb Santé, action sociale	Nb de Commerce	Nb de Services aux particuliers	Nb institution de Education, santé, action sociale, administration	Score Croissance Population	Score Croissance Entrepreneuriale
0	01001	57.0	23.0	114	114.56713	9	14	7	7	0.092860	...	-1	117	3.0	1.0	0.0	9350	3372	15105	72.13115	0.01585
1	01002	45.0	4.0	143	143.71141	31	36	18	18	0.099229	...	0	110	0.0	0.0	0.0	9350	3372	15105	72.13115	0.00173
2	01004	634.0	828.0	366	367.82080	31	36	18	18	0.099229	...	-55	250	113.0	41.0	118.0	9350	3372	15105	72.95082	0.38471
3	01005	113.0	62.0	132	132.65668	12	12	6	6	1.000000	...	-3	127	5.0	2.0	7.0	9350	3372	15105	72.13115	0.02824
4	01006	42.0	1.0	121	121.60196	26	21	10	10	0.100905	...	0	109	0.0	0.0	0.0	9350	3372	15105	67.62295	0.00000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36672	97420	657.0	534.0	186	186.92533	12	12	6	6	1.000000	...	-265	194	69.0	13.0	66.0	574	117	1102	76.22951	0.43688
36673	97421	228.0	212.0	91	91.45271	12	12	6	6	1.000000	...	-117	104	25.0	1.0	29.0	574	117	1102	76.63934	0.13083
36674	97422	2689.0	2586.0	837	841.16396	12	12	6	6	1.000000	...	-734	-512	334.0	116.0	301.0	574	117	1102	75.40984	2.09129
36675	97423	222.0	210.0	88	88.43779	12	12	6	6	1.000000	...	-88	102	32.0	5.0	30.0	574	117	1102	75.81967	0.10547
36676	97424	163.0	239.0	84	84.41789	12	12	6	6	1.000000	...	-81	95	22.0	5.0	29.0	574	117	1102	75.81967	0.08098

36677 rows × 42 columns

Cinemas

data_cine = pd.read_excel('https://www.data.gouv.fr/fr/datasets/r/cdb918e7-7f1a-44fc-bf6f-c59d1614ed6d', sheet_name = "2023")
data_cine.drop(index= [0,1,2], errors = 'ignore', inplace = True)
data_cine.columns = data_cine.iloc[0]
data_cine = data_cine[1:]
data_cine = data_cine.reset_index(drop=True)
data_cine["DEPCOM"] = data_cine["DEPCOM"].astype(str).str.strip()
data_cine.to_csv('data_cine_finale.csv')
data_cine.head()

3	NAutoC	NomEtab	Ecrans	fauteuils	DEPCOM	COMMUNE	genre	AE	3D
0	31	UGC NORMANDIE	4	1533	75108	Paris 8e Arrondissement	FIXE	NON	OUI
1	35	BALZAC	3	589	75108	Paris 8e Arrondissement	FIXE	OUI	OUI
2	54	GAUMONT CHAMPS ELYSEES MARIGNAN	6	1597	75108	Paris 8e Arrondissement	FIXE	NON	NON
3	55	ELYSEES LINCOLN	3	448	75108	Paris 8e Arrondissement	FIXE	OUI	NON
4	204	UGC OPERA	4	722	75109	Paris 9e Arrondissement	FIXE	NON	NON

Museums

data_muse = pd.read_csv(
    "https://data.culture.gouv.fr/api/explore/v2.1/catalog/datasets/musees-de-france-base-museofile/exports/csv?use_labels=true",
    on_bad_lines='skip',
    sep = ';'
)
data_muse.dropna(axis=1, how='all', inplace=False)
data_muse.to_csv('data_muse_finale.csv')
data_muse.head()

	Identifiant	Nom_officiel	Adresse	Lieu	Code_postal	Ville	Région	Departement	URL	Telephone	...	Thèmes	Artiste	Personnage_phare	Intérêt	Protection_batiment	Protection_espace	Refmer	Annee_creation	Date_de_mise_a_jour	Coordonnees
0	M0001	musée de la Folie Marco	30 rue du docteur Sultzer	NaN	67140	Barr	Grand Est	Bas-Rhin	www.musee-foliemarco.com	03 88 08 94 72	...	Arts décoratifs : Mobilier, Objets d'art	NaN	NaN	Demeure patricienne d'architecture française d...	Protégé au titre des Monuments historiques : h...	Abords d'un Monument historique. Site inscrit.	PA00084599;IA00115077	1960	2024-01-25	48.410166, 7.451102
1	M0031	musée historique	64 rue du général de Gaulle	NaN	68240	Kaysersberg	Grand Est	Haut-Rhin	www.kaysersberg-vignoble.fr/musee-historique	03 89 78 11 11	...	Archéologie nationale : Protohistoire;Art reli...	NaN	NaN	Maison patricienne construite en 1521 par René...	Protégé au titre des Monuments historiques : h...	Site inscrit.	PA00085485;IA68000639	NaN	2024-01-25	48.139072, 7.261441
2	M0036	musée Français du Chemin de Fer	2 rue Alfred de Glehn	NaN	68200	Mulhouse	Grand Est	Haut-Rhin	www.citedutrain.com/	03 89 42 83 33 / 25 67	...	Beaux-Arts : Dessin, Estampe et Affiche, Peint...	NaN	NaN	NaN	NaN	Site patrimonial remarquable.	SPR4400043;SPR4400044	1971	2024-01-25	47.732372, 7.312824
3	M0038	musée du Sapeur-Pompier	12-14 rue de Luppach	NaN	68480	Vieux-Ferrette	Grand Est	Haut-Rhin	www.musee-sapeur-pompier.fr/	06 11 68 75 13	...	Autre(s), (Sapeurs-pompiers),	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2024-02-07	47.504301, 7.304287
4	M0041	musée Paysan	10 rue principale	NaN	68480	Oltingue	Grand Est	Haut-Rhin	musee.paysan.free.fr/	03 89 40 79 24	...	Art religieux (Art chrétien);Ethnologie : Cost...	NaN	NaN	Maison du XVI - XVIIe siècles. Rez-de-chaussée...	NaN	NaN	NaN	NaN	2024-02-07	47.492827, 7.391275

5 rows × 24 columns

Festivals

data_festivals = pd.read_csv("panorama-des-festivals.csv", on_bad_lines = 'skip', sep = ';')
data_festivals.to_csv('data_festivals_finale.csv')
data_festivals.head()

	Nom de la manifestation	Région	Domaine	Complément domaine	Département	Périodicité	Mois habituel de début	Site web	N° Identification	Commune principale	...	Commentaires	N° de l'édition 2019	Check édition	Mois indicatif en chiffre, y compris double mois	Mois indicatif	Date début ancien	Date de fin ancien	Soutien 2017 MCC à la structure	Part festival sur soutien à la structure	Enquête DRAC 2017
0	FESTIVAL LES VAGAMONDES	Grand Est	Transdisciplinaire	NaN	68	Annuelle	01 (janvier)	www.lafilature.org	FA007	MULHOUSE	...	NaN	7.0	0.0	1.0	NaN	2019-01-09	2019-01-19	NaN	NaN	NaN
1	LA FONTAINE DU RIRE	Bourgogne-Franche-Comté	Divers Spectacle vivant	Humour	21	Annuelle	01 (janvier)	http://www.la-tete-de-mule.fr/	BH004	DIJON	...	NaN	31.0	0.0	1.0	NaN	2019-01-11	2019-04-13	NaN	NaN	NaN
2	FLAMENCO DE NÎMES	Occitanie	Musiques actuelles	Musiques traditionnelles et du monde	30	Annuelle	01 (janvier)	www.theatredenimes.com	KD056	NIMES	...	NaN	29.0	0.0	1.0	NaN	2019-01-11	2019-01-19	NaN	NaN	NaN
3	Biennale Internationale des Arts du Cirque Pro...	Provence-Alpes-Côte d'Azur	Cirque et Arts de la rue	Cirque	13	Biennale années impaires	01 (janvier)	http://www.biennale-cirque.com/fr/	MM009	MARSEILLE	...	3ème édition en 2019	27.0	-22.0	1.0	NaN	2019-01-11	2019-02-10	NaN	NaN	NaN
4	Festival des Petites formes	Martinique	Théâtre	NaN	972	Annuelle	01 (janvier)	http://tropiques-atrium.fr/	QG001	FORT DE FRANCE	...	NaN	NaN	2020.0	1.0	NaN	2019-01-15	2019-01-27	NaN	NaN	NaN

5 rows × 37 columns

Stations

gare1 = pd.read_csv("gares-de-voyageurs.csv", sep=";")
gare1

	Nom	Trigramme	Segment(s) DRG	Position géographique	Code commune	Code(s) UIC
0	Abancourt	ABT	C	49.6852237, 1.7743058	60001	87313759
1	Abbaretz	AAR	C	47.5546432, -1.5244159	44001	87481614
2	Abbeville	ABB	B	50.10221, 1.82449	80001	87317362
3	Ablon-sur-Seine	ABL	B	48.725468, 2.419151	94001	87545269
4	Achères Grand Cormier	GCR	B	48.9551835, 2.0919031	78551	87386052
...	...	...	...	...	...	...
2876	Ygos-Saint-Saturnin	YGV	C	43.9781851, -0.7361528	40333	87671487
2877	Ytrac	YTC	C	44.9106891, 2.3644474	15267	87645101
2878	Yvetot	YVT	B	49.622035, 0.750115	76758	87413385
2879	Zetting	ZGS	C	49.0783736, 7.13454	57760	87193649
2880	Zillisheim	ZIL	C	47.6943143, 7.2945376	68384	87182196

2881 rows × 6 columns

gare = gare1.drop(["Trigramme", "Segment(s) DRG","Position géographique","Code(s) UIC"], axis=1)
gare

	Nom	Code commune
0	Abancourt	60001
1	Abbaretz	44001
2	Abbeville	80001
3	Ablon-sur-Seine	94001
4	Achères Grand Cormier	78551
...	...	...
2876	Ygos-Saint-Saturnin	40333
2877	Ytrac	15267
2878	Yvetot	76758
2879	Zetting	57760
2880	Zillisheim	68384

2881 rows × 2 columns

gare.isnull().sum()

	0
Nom	0
Code commune	0

dtype: int64

gare.to_csv("cleaned_gare")

Unemployment

unemployment1 = pd.read_csv("base-cc-emploi-pop-active-2021.CSV", sep=";", low_memory=False )

unemployment1

	CODGEO	P21_POP1564	P21_POP1524	P21_POP2554	P21_POP5564	P21_H1564	P21_H1524	P21_H2554	P21_H5564	P21_F1564	...	C10_AGRILT_NSAL	C10_INDUSLT_NSAL	C10_CONSTLT_NSAL	C10_CTSLT_NSAL	C10_APESASLT_NSAL	C10_AGRILT_FNSAL	C10_INDUSLT_FNSAL	C10_CONSTLT_FNSAL	C10_CTSLT_FNSAL	C10_APESASLT_FNSAL
0	01001	508.512208	64.249709	318.299551	125.962948	253.798073	31.236703	159.078594	63.482776	254.714135	...	16.082051	0.000000	0.000000	0.000000	0.000000	8.041026	0.000000	0.000000	0.000000	0.000000
1	01002	163.031833	25.369864	113.047136	24.614834	86.645949	17.294338	57.896654	11.454957	76.385884	...	8.230216	0.000000	0.000000	0.000000	0.000000	4.115108	0.000000	0.000000	0.000000	0.000000
2	01004	9354.791537	1989.441923	5592.059164	1773.290451	4630.671492	1018.854165	2794.541306	817.276020	4724.120046	...	4.011494	44.784099	72.645873	351.228294	134.275097	0.000000	1.046354	4.000000	132.729201	84.970705
3	01005	1215.309881	170.389906	783.645309	261.274666	604.818834	84.008557	386.665526	134.144750	610.491047	...	0.000000	12.392645	15.751651	43.958781	4.000000	0.000000	4.196322	0.000000	19.905270	4.000000
4	01006	71.368421	7.929825	39.649123	23.789474	41.631579	4.956140	22.798246	13.877193	29.736842	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
34958	97420	16108.505669	3506.581393	9542.715401	3059.208875	7692.589219	1801.706308	4360.350565	1530.532345	8415.916450	...	167.608595	44.170260	96.166186	257.949321	77.770915	7.444948	10.042471	0.000000	85.039400	32.537541
34959	97421	4641.841180	916.113174	2827.351696	898.376310	2388.191168	483.479763	1429.038075	475.673330	2253.650012	...	173.730964	29.348228	37.679607	91.476776	38.726398	26.842714	6.510702	0.000000	39.008800	18.920603
34960	97422	52788.604310	11603.100415	30856.299799	10329.204096	25196.104893	6005.654209	14341.624201	4848.826483	27592.499417	...	907.847602	176.917241	449.935919	1125.270010	413.117857	204.157456	29.936315	37.348639	405.712175	156.281289
34961	97423	4582.757335	848.390699	2796.919398	937.447238	2287.976263	434.010089	1360.293666	493.672509	2294.781072	...	62.508538	12.000000	21.010108	73.017514	58.970027	12.000000	4.000000	0.000000	14.531272	18.977908
34962	97424	3497.850587	597.871299	2049.401472	850.577816	1835.910895	336.912412	1035.918883	463.079599	1661.939692	...	23.843107	12.810154	25.620308	81.895747	22.104665	4.270051	4.270051	0.000000	45.204064	4.270051

34963 rows × 354 columns

columns_to_keep = ['CODGEO','P21_POP1564','P21_POP1524', 'P21_POP2554', 'P21_CHOM1564', 'P21_CHOM1524', 'P21_CHOM2554']
unemployment = unemployment1[columns_to_keep]
unemployment

	CODGEO	P21_POP1564	P21_POP1524	P21_POP2554	P21_CHOM1564	P21_CHOM1524	P21_CHOM2554
0	01001	508.512208	64.249709	318.299551	27.166576	3.891738	18.456380
1	01002	163.031833	25.369864	113.047136	2.147689	0.000000	1.008105
2	01004	9354.791537	1989.441923	5592.059164	969.687159	223.931026	636.631136
3	01005	1215.309881	170.389906	783.645309	62.209446	7.481076	42.712138
4	01006	71.368421	7.929825	39.649123	4.956140	0.991228	2.973684
...	...	...	...	...	...	...	...
34958	97420	16108.505669	3506.581393	9542.715401	3475.882292	772.435192	2281.243797
34959	97421	4641.841180	916.113174	2827.351696	1246.514898	218.249094	845.943441
34960	97422	52788.604310	11603.100415	30856.299799	11889.476011	2115.543896	8039.959976
34961	97423	4582.757335	848.390699	2796.919398	1208.053406	207.614211	817.632062
34962	97424	3497.850587	597.871299	2049.401472	907.509828	131.250554	608.271781

34963 rows × 7 columns

unemployment.isnull().sum()

	0
CODGEO	0
P21_POP1564	3
P21_POP1524	3
P21_POP2554	3
P21_CHOM1564	3
P21_CHOM1524	3
P21_CHOM2554	3

dtype: int64

unemployment.dropna(inplace = True)
unemployment.isnull().sum()

<ipython-input-68-2a76a3bc6851>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  unemployment.dropna(inplace = True)

	0
CODGEO	0
P21_POP1564	0
P21_POP1524	0
P21_POP2554	0
P21_CHOM1564	0
P21_CHOM1524	0
P21_CHOM2554	0

dtype: int64

unemployment.to_csv("cleaned_unemployment1")

Real Estate

real_estate = pd.read_csv("indicateur immobilier.csv", sep=",")

real_estate

	Unnamed: 0	INSEE_COM	Annee	Nb_mutations	NbMaisons	NbApparts	propmaison	propappart	PrixMoyen	Prixm2Moyen	SurfaceMoy
0	1	01001	2023	8	8.0	0.0	100.000000	0.000000	269375.000000	2249.500000	118.125000
1	2	01002	2023	2	2.0	0.0	100.000000	0.000000	199350.000000	1913.500000	96.000000
2	3	01004	2023	158	89.0	69.0	56.329114	43.670886	210114.639241	2399.620253	89.639241
3	4	01005	2023	27	22.0	5.0	81.481481	18.518519	357840.259259	3287.962963	109.185185
4	5	01007	2023	39	38.0	1.0	97.435897	2.564103	231436.461538	2274.871795	105.230769
...	...	...	...	...	...	...	...	...	...	...	...
30006	30007	95678	2023	10	8.0	2.0	80.000000	20.000000	375823.000000	4292.600000	85.700000
30007	30008	95680	2023	144	45.0	99.0	31.250000	68.750000	174609.027778	2621.680556	68.312500
30008	30009	95682	2023	1	1.0	0.0	100.000000	0.000000	200000.000000	3333.000000	60.000000
30009	30010	95690	2023	3	3.0	0.0	100.000000	0.000000	391683.333333	2706.000000	147.666667
30010	30011	NaN	2023	8530	NaN	NaN	49.472450	50.527550	227263.892263	3154.719695	73.271278

30011 rows × 11 columns

real_estate.isnull().sum()

	0
Unnamed: 0	0
INSEE_COM	1
Annee	0
Nb_mutations	0
NbMaisons	1
NbApparts	1
propmaison	0
propappart	0
PrixMoyen	0
Prixm2Moyen	0
SurfaceMoy	0

dtype: int64

real_estate.dropna(subset = ['INSEE_COM'], inplace = True)
real_estate.isnull().sum()

	0
Unnamed: 0	0
INSEE_COM	0
Annee	0
Nb_mutations	0
NbMaisons	0
NbApparts	0
propmaison	0
propappart	0
PrixMoyen	0
Prixm2Moyen	0
SurfaceMoy	0

dtype: int64

real_estate.to_csv("cleaned_realestate.csv", index=False)

In this part we only display our clean datasets

MAXIM'S DATASETS

df = pd.read_csv("niveau_de_vie_médian_2014.csv", sep = ";")

df["MED14"] = df["MED14"].str.replace(',', '.')
df["MED14"] = df["MED14"].astype(float)

df

	CODGEO	LIBGEO	MED14
0	01001	L'Abergement-Clémenciat	21576.7
1	01002	L'Abergement-de-Varey	21672.9
2	01004	Ambérieu-en-Bugey	19756.1
3	01005	Ambérieux-en-Dombes	23204.8
4	01006	Ambléon	22157.5
...	...	...	...
36616	97420	Sainte-Suzanne	14125.6
36617	97421	Salazie	10827.4
36618	97422	Le Tampon	13349.4
36619	97423	Les Trois-Bassins	13104.5
36620	97424	Cilaos	11545.0

36621 rows × 3 columns

df2 = pd.read_csv("batiment_historique_finale.csv", sep=",")

df2

	Unnamed: 0	Commune	INSEE	dénomination	titre courant	statut propriété
0	0	'Abergement-Clémenciat (L'')'	1001	site archéologique	Site médiéval du Vieux Bourg	'propriété d''une personne privée'
1	1	Amareins-Francheleins-Cesseins	1165	église	Eglise	propriété de la commune
2	2	Ambronay	1007	abbaye	Ancienne abbaye d’Ambronay	propriété de la commune
3	3	Ambronay	1007	fort	site archéologique	Bastide de Gironville dite Fort Sarrazin
4	4	Ambérieu-en-Bugey	1004	château	site archéologique	Château des Allymes
...	...	...	...	...	...	...
44313	44313	Saint-Pierre	97502	maison	'Maison dite " de Saint-Pierre Animation " sit...	'propriété d''une association'
44314	44314	Saint-Pierre	97502	maison	Maison dite " maison Jézéquel "	' située sur l''Ile-aux-Marins'
44315	44315	Saint-Pierre	97502	maison	saline	'Maison et saline Morel situées sur l''île-aux...
44316	44316	Dzaoudzi	97608	demeure	Ancienne résidence des Gouverneurs	située au rocher de Dzaoudzi
44317	44317	Tsingoni	97617	mosquée	Mosquée du 16e siècle	propriété de la commune

44318 rows × 6 columns

df3 = pd.read_csv("crime_delit.csv", sep=",", low_memory=False)

df3

	Unnamed: 0	CODGEO_2024	classe	unité.de.compte
0	0	01001	Coups et blessures volontaires	victime
1	1	01001	Coups et blessures volontaires intrafamiliaux	victime
2	2	01001	Autres coups et blessures volontaires	victime
3	3	01001	Violences sexuelles	victime
4	4	01001	Vols avec armes	infraction
...	...	...	...	...
3917755	3917755	97617	Vols dans les véhicules	véhicule
3917756	3917756	97617	Vols d'accessoires sur véhicules	véhicule
3917757	3917757	97617	Destructions et dégradations volontaires	infraction
3917758	3917758	97617	Trafic de stupéfiants	Mis en cause
3917759	3917759	97617	Usage de stupéfiants	Mis en cause

3917760 rows × 4 columns

df3.columns

Index(['Unnamed: 0', 'CODGEO_2024', 'classe', 'unité.de.compte'], dtype='object')

df4 = pd.read_csv("hopitaux.csv", sep=",", low_memory=False)

df4

	Unnamed: 0	denominationUniteLegale	codePostalEtablissement	libelleCommuneEtablissement	codeCommuneEtablissement
0	0	SA SAINTE-ISABELLE	80100.0	ABBEVILLE	80001
1	1	CENTRE DES CARMES	4510.0	AIGLUN	04001
2	2	CLINIQUE LES ROSIERS	21000.0	DIJON	21231
3	3	MONTSINERY	6600.0	ANTIBES	06004
4	4	VALICELLI CLINIQUE DIABETE ET NUTRITION	20117.0	OCANA	2A181
...	...	...	...	...	...
9725	9725	MAISON DE REPOS PRIMEROSE	40150.0	SOORTS-HOSSEGOR	40304
9726	9726	CLINIQUE JEAN LE BON	40100.0	DAX	40088
9727	9727	CLINIQUE DE LA MARCHE	23000.0	GUERET	23096
9728	9728	CLINIQUE VELPEAU	37100.0	TOURS	37261
9729	9729	CENTRE READAPTATION FONCTION ALBITRECCIA	20166.0	ALBITRECCIA	2A008

9730 rows × 5 columns

df5 = pd.read_csv("école_premier_seconde_deg.csv", sep=',')

df5

	Unnamed: 0	appellation_officielle	denomination_principale	patronyme_uai	adresse_uai	code_postal_uai	localite_acheminement_uai	libelle_commune	nature_uai_libe	code_commune
0	0	Ecole primaire Jean Lelievre	ECOLE PRIMAIRE PUBLIQUE	JEAN LELIEVRE	1 rue de l'Eglise	28500	MARVILLE MOUTIERS BRULE	Marville-Moutiers-Brûlé	ECOLE DE NIVEAU ELEMENTAIRE	28239
1	1	Ecole primaire	ECOLE PRIMAIRE	TREON	4 rue Lucien Rialet	28500	TREON	Tréon	ECOLE DE NIVEAU ELEMENTAIRE	28394
2	2	Ecole élémentaire Louis Pergaud	ECOLE ELEMENTAIRE PUBLIQUE	LOUIS PERGAUD	1 rue de l'Epinay	28500	VERNOUILLET	Vernouillet	ECOLE DE NIVEAU ELEMENTAIRE	28404
3	3	Ecole primaire	ECOLE PRIMAIRE PUBLIQUE	BOISVILLE-LA-SAINT-PERE-ALLONES	1 rue du Stade	28150	BOISVILLE LA ST PERE	Boisville-la-Saint-Père	ECOLE DE NIVEAU ELEMENTAIRE	28047
4	4	Ecole primaire	ECOLE PRIMAIRE PUBLIQUE	NaN	3 rue de la République	28150	OUARVILLE	Ouarville	ECOLE DE NIVEAU ELEMENTAIRE	28291
...	...	...	...	...	...	...	...	...	...	...
64410	64410	CETAD DE TAIOHAE	CETAD	CETAD DE TAIOHAE	NaN	98742	TAIPIVAI	Nuku-hiva	CETAD (TOM)	98731
64411	64411	LYCEE POLYVALENT TUIANU LE GAYIC	LYCEE POLYVALENT	TUIANU LE GAYIC	Quartier	98712	PAPARA	Papara	LYCEE POLYVALENT	98734
64412	64412	CETAD DE HAO	CETAD	CETAD DE HAO	NaN	98767	OTEPA	Hao	CETAD (TOM)	98720
64413	64413	Ecole primaire publique Matie Roa	ECOLE PRIMAIRE PUBLIQUE	MATIE ROA	Village HAAMENE PK 21	98733	TIVA	Tahaa	ECOLE DE NIVEAU ELEMENTAIRE	98745
64414	64414	Ecole maternelle de FAAROA	ECOLE MATERNELLE PUBLIQUE	FAAROA	Lieu-dit PK 13 BAIE DE FAAROA	98735	AVERA	Taputapuatea	ECOLE MATERNELLE	98750

64415 rows × 10 columns

df_pop_finale = pd.read_csv("population par commune.csv", sep=',')

df_pop_finale

	Unnamed: 0	CODGEO	P21_POP	P21_POP0014	P21_POP1529	P21_POP3044	P21_POP4559	P21_POP6074	P21_POP7589	P21_POP90P
0	0	1001	832.0	158.552045	100.025669	152.787222	198.071134	149.720218	67.098389	5.745324
1	1	1002	267.0	57.046286	32.439093	57.272224	58.101506	40.504740	19.768434	1.867717
2	2	1004	14854.0	2789.758194	3081.375266	2740.192625	2632.565876	2269.033228	1152.137702	188.937109
3	3	1005	1897.0	367.334805	289.213773	388.963943	415.518703	301.217072	123.672440	11.079265
4	4	1006	113.0	9.912281	13.877193	19.824561	23.789474	37.666667	7.929825	0.000000
...	...	...	...	...	...	...	...	...	...	...
34958	34958	97420	24293.0	5564.329899	4853.335481	4556.391863	5370.832301	2864.635451	997.155641	86.319364
34959	34959	97421	7243.0	1700.208360	1326.662599	1298.668993	1620.251575	896.342643	371.422147	29.443683
34960	34960	97422	81943.0	18285.741483	16318.584869	15214.365223	16589.370571	11142.902995	4009.875265	382.159595
34961	34961	97423	6899.0	1342.305384	1253.046383	1257.043099	1694.133409	950.602949	352.302838	49.565938
34962	34962	97424	5390.0	1082.145083	844.494729	969.707026	1314.192831	832.305572	319.769028	27.385731

34963 rows × 10 columns

df_activite_eco = pd.read_csv("activité economique par commune.csv")
df_activite_eco= df_activite_eco.drop(['Unnamed: 0'], axis = 1)

<ipython-input-102-0003b39a25bb>:1: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.
  df_activite_eco = pd.read_csv("activité economique par commune.csv")

df_activite_eco

	CODGEO	Dynamique Entrepreneuriale	Dynamique Entrepreneuriale Service et Commerce	Synergie Médicale COMMUNE	Indice Synergie Médicale	Nb Omnipraticiens BV	Nb Infirmiers Libéraux BV	Nb dentistes Libéraux BV	Nb pharmaciens Libéraux BV	Densité Médicale BV	...	Dynamique Démographique INSEE	Capacité Fiscale	Nb Education, santé, action sociale	Nb Services personnels et domestiques	Nb Santé, action sociale	Nb de Commerce	Nb de Services aux particuliers	Nb institution de Education, santé, action sociale, administration	Score Croissance Population	Score Croissance Entrepreneuriale
0	01001	57.0	23.0	114	114.56713	9	14	7	7	0.092860	...	-1	117	3.0	1.0	0.0	9350	3372	15105	72.13115	0.01585
1	01002	45.0	4.0	143	143.71141	31	36	18	18	0.099229	...	0	110	0.0	0.0	0.0	9350	3372	15105	72.13115	0.00173
2	01004	634.0	828.0	366	367.82080	31	36	18	18	0.099229	...	-55	250	113.0	41.0	118.0	9350	3372	15105	72.95082	0.38471
3	01005	113.0	62.0	132	132.65668	12	12	6	6	1.000000	...	-3	127	5.0	2.0	7.0	9350	3372	15105	72.13115	0.02824
4	01006	42.0	1.0	121	121.60196	26	21	10	10	0.100905	...	0	109	0.0	0.0	0.0	9350	3372	15105	67.62295	0.00000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36672	97420	657.0	534.0	186	186.92533	12	12	6	6	1.000000	...	-265	194	69.0	13.0	66.0	574	117	1102	76.22951	0.43688
36673	97421	228.0	212.0	91	91.45271	12	12	6	6	1.000000	...	-117	104	25.0	1.0	29.0	574	117	1102	76.63934	0.13083
36674	97422	2689.0	2586.0	837	841.16396	12	12	6	6	1.000000	...	-734	-512	334.0	116.0	301.0	574	117	1102	75.40984	2.09129
36675	97423	222.0	210.0	88	88.43779	12	12	6	6	1.000000	...	-88	102	32.0	5.0	30.0	574	117	1102	75.81967	0.10547
36676	97424	163.0	239.0	84	84.41789	12	12	6	6	1.000000	...	-81	95	22.0	5.0	29.0	574	117	1102	75.81967	0.08098

36677 rows × 42 columns

MARIA'S DATASETS

df_unemployement = pd.read_csv("/content/cleaned_unemployment")

df_unemployement

	Unnamed: 0	CODGEO	P21_POP1564	P21_POP1524	P21_POP2554	P21_CHOM1564	P21_CHOM1524	P21_CHOM2554
0	0	01001	508.512208	64.249709	318.299551	27.166576	3.891738	18.456380
1	1	01002	163.031833	25.369864	113.047136	2.147689	0.000000	1.008105
2	2	01004	9354.791537	1989.441923	5592.059164	969.687159	223.931026	636.631136
3	3	01005	1215.309881	170.389906	783.645309	62.209446	7.481076	42.712138
4	4	01006	71.368421	7.929825	39.649123	4.956140	0.991228	2.973684
...	...	...	...	...	...	...	...	...
34955	34958	97420	16108.505669	3506.581393	9542.715401	3475.882292	772.435192	2281.243797
34956	34959	97421	4641.841180	916.113174	2827.351696	1246.514898	218.249094	845.943441
34957	34960	97422	52788.604310	11603.100415	30856.299799	11889.476011	2115.543896	8039.959976
34958	34961	97423	4582.757335	848.390699	2796.919398	1208.053406	207.614211	817.632062
34959	34962	97424	3497.850587	597.871299	2049.401472	907.509828	131.250554	608.271781

34960 rows × 8 columns

df7 = pd.read_csv("cleaned_realestate.csv")

df7.rename(columns={"INSEE_COM": "CODGEO"}, inplace=True)
df7

	Unnamed: 0	CODGEO	Annee	Nb_mutations	NbMaisons	NbApparts	propmaison	propappart	PrixMoyen	Prixm2Moyen	SurfaceMoy
0	1	01001	2023	8	8.0	0.0	100.000000	0.000000	269375.000000	2249.500000	118.125000
1	2	01002	2023	2	2.0	0.0	100.000000	0.000000	199350.000000	1913.500000	96.000000
2	3	01004	2023	158	89.0	69.0	56.329114	43.670886	210114.639241	2399.620253	89.639241
3	4	01005	2023	27	22.0	5.0	81.481481	18.518519	357840.259259	3287.962963	109.185185
4	5	01007	2023	39	38.0	1.0	97.435897	2.564103	231436.461538	2274.871795	105.230769
...	...	...	...	...	...	...	...	...	...	...	...
30005	30006	95676	2023	2	2.0	0.0	100.000000	0.000000	371650.000000	2555.000000	147.000000
30006	30007	95678	2023	10	8.0	2.0	80.000000	20.000000	375823.000000	4292.600000	85.700000
30007	30008	95680	2023	144	45.0	99.0	31.250000	68.750000	174609.027778	2621.680556	68.312500
30008	30009	95682	2023	1	1.0	0.0	100.000000	0.000000	200000.000000	3333.000000	60.000000
30009	30010	95690	2023	3	3.0	0.0	100.000000	0.000000	391683.333333	2706.000000	147.666667

30010 rows × 11 columns

df8 = pd.read_csv("cleaned_gare")

df8

	Unnamed: 0	Nom	Code commune
0	0	Abancourt	60001
1	1	Abbaretz	44001
2	2	Abbeville	80001
3	3	Ablon-sur-Seine	94001
4	4	Achères Grand Cormier	78551
...	...	...	...
2876	2876	Ygos-Saint-Saturnin	40333
2877	2877	Ytrac	15267
2878	2878	Yvetot	76758
2879	2879	Zetting	57760
2880	2880	Zillisheim	68384

2881 rows × 3 columns

DAPHNE'S DATASETS

data_cine_finale = pd.read_csv("data_cine_finale.csv", sep=",")
cine_reduit = data_cine_finale.groupby("DEPCOM").size().reset_index(name="Number_of_Cinemas")
print(cine_reduit)

     DEPCOM  Number_of_Cinemas
0     01004                  1
1     01033                  1
2     01034                  1
3     01053                  2
4     01093                  1
...     ...                ...
1696  95555                  1
1697  95572                  1
1698  95585                  1
1699  95607                  1
1700  95637                  1

[1701 rows x 2 columns]

data_muse_finale = pd.read_csv("data_muse_finale.csv", sep=",")
muse_reduit = data_muse_finale.groupby("Code_postal").size().reset_index(name="Number_of_Museums")
print(muse_reduit)

    Code_postal  Number_of_Museums
0         01000                  3
1         01130                  1
2         01190                  1
3         01240                  1
4         01260                  1
..          ...                ...
889       97424                  1
890       97435                  1
891       97450                  1
892       97500                  1
893       97615                  1

[894 rows x 2 columns]

data_festivals_finals = pd.read_csv("data_festivals_finale.csv", sep=",")
fest_reduit = data_festivals_finals.groupby("Code postal").size().reset_index(name="Number_of_Festivals")
print(fest_reduit)

      Code postal  Number_of_Festivals
0            1000                    7
1            1100                    1
2            1110                    1
3            1120                    1
4            1170                    1
...           ...                  ...
1438        97670                    1
1439        98714                    3
1440        98810                    1
1441        98822                    1
1442        98880                    1

[1443 rows x 2 columns]

cine_reduit.rename(columns={"DEPCOM": "CODGEO"}, inplace=True)
fest_reduit.rename(columns={"Code postal": "CODGEO"}, inplace=True)
muse_reduit.rename(columns={"Code_postal": "CODGEO"}, inplace=True)

MERGING

Now that we have all of our datasets cleaned, containing only the values that matter, we want to merge them in order to build the dataframe we’ll apply our machine learning models to.

To merge correctly, we rename all the columns containing the zip code “CODGEO” and we make sure they are all on the same format (string).

After that we do the merging on the left to merge on CODGEO, the zipcodes, and once we get the full datasets containing each counts of the different infrastructures and indicators we replace the Nan values by 0.

df['CODGEO'] = df['CODGEO'].astype(str).str.lstrip('0')
df2['INSEE'] = df2['INSEE'].astype(str).str.lstrip('0')
df3['CODGEO_2024']= df3['CODGEO_2024'].astype(str).str.lstrip('0')
df4['codeCommuneEtablissement']= df4['codeCommuneEtablissement'].astype(str).str.lstrip('0')
df5['code_commune']= df5['code_commune'].astype(str).str.lstrip('0')
df_pop_finale['CODGEO'] = df_pop_finale['CODGEO'].astype(str).str.lstrip('0')
df_activite_eco['CODGEO'] = df_activite_eco['CODGEO'].astype(str).str.lstrip('0')
df_unemployement['CODGEO'] = df_unemployement['CODGEO'].astype(str).str.lstrip('0')
df7['CODGEO'] = df7['CODGEO'].astype(str).str.lstrip('0')
#df8['Code commune'] = df8['Code commune'].astype.str.lstrip('0') déjà au bon format
#df9['CODE_INSEE'] = df9 ['CODE_INSEE'].astype.str.lstrip('0') déjà au bon format
cine_reduit['CODGEO'] = cine_reduit['CODGEO'].astype(str).str.lstrip('0')
muse_reduit['CODGEO'] = muse_reduit['CODGEO'].astype(str).str.lstrip('0')
fest_reduit['CODGEO'] = fest_reduit['CODGEO'].astype(str).str.lstrip('0')

#bâtiments historiques
batiments_count = df2['INSEE'].value_counts().reset_index()
batiments_count.columns = ['CODGEO', 'nombre_batiments_historique']

#hopitaux
hopitaux_count = df4['codeCommuneEtablissement'].value_counts().reset_index()
hopitaux_count.columns = ['CODGEO', 'nombre_hopitaux']

#crime
crime_count = df3['CODGEO_2024'].value_counts().reset_index()
crime_count.columns = ['CODGEO', 'nombre_crime']

#ecole
ecole_count = df5['code_commune'].value_counts().reset_index()
ecole_count.columns = ['CODGEO', 'nombre_ecole']

#Population

#activité economique


#unemployment
df7.rename(columns={"INSEE_COM": "CODGEO"}, inplace=True)


#real_estate


#gare
gare_count = df8['Code commune'].value_counts().reset_index()
gare_count.columns = ['CODGEO', 'nombre_gare']
gare_count['CODGEO'] = gare_count['CODGEO'].astype(str)


#cinemas

cine_reduit.rename(columns={"Number_of_Cinemas": "nombre_cinemas"}, inplace=True)

#festivals

fest_reduit.rename(columns={"Number_of_Festivals": "nombre_festivals"}, inplace=True)

#museums

muse_reduit.rename(columns={"Number_of_Museums": "nombre_musees"}, inplace=True)


# Fusion des données : ajout de la colonne nombre_batiments au dataset des communes
df_communes = df.merge(batiments_count, on='CODGEO', how='left')
df_communes = df_communes.merge(hopitaux_count, on='CODGEO', how='left')
df_communes = df_communes.merge(crime_count, on='CODGEO', how='left')
df_communes = df_communes.merge(ecole_count, on='CODGEO', how='left')
df_communes = df_communes.merge(df_pop_finale, on='CODGEO', how='left')
df_communes = df_communes.merge(df_unemployement, on='CODGEO', how='left')
df_communes = df_communes.merge(cine_reduit, on='CODGEO', how='left')
df_communes = df_communes.merge(muse_reduit, on='CODGEO', how='left')
df_communes = df_communes.merge(fest_reduit, on='CODGEO', how='left')
df_communes = df_communes.merge(df7, on='CODGEO', how='left')
df_communes = df_communes.merge(gare_count, on='CODGEO', how='left')
df_communes = df_communes.merge(df_activite_eco, on='CODGEO', how='left')

# Remplacement des valeurs NaN par 0 (si une commune n'a aucun bâtiment historique)
df_communes['nombre_batiments_historique'] = df_communes['nombre_batiments_historique'].fillna(0).astype(int)
df_communes['nombre_hopitaux'] = df_communes['nombre_hopitaux'].fillna(0).astype(int)
df_communes['nombre_crime'] = df_communes['nombre_crime'].fillna(0).astype(int)
df_communes['nombre_ecole'] = df_communes['nombre_ecole'].fillna(0).astype(int)
df_communes['nombre_cinemas'] = df_communes['nombre_cinemas'].fillna(0).astype(int)
df_communes['nombre_musees'] = df_communes['nombre_musees'].fillna(0).astype(int)
df_communes['nombre_festivals'] = df_communes['nombre_festivals'].fillna(0).astype(int)
df_communes['nombre_gare'] = df_communes['nombre_gare'].fillna(0).astype(int)


# Sauvegarde du résultat dans un nouveau fichier CSV
df_communes.to_csv('df_communes.csv', index=False)

Une nouvelle colonne 'nombre_batiments' a été ajoutée au dataset des communes.

Here is the final dataset before pre-processing :

df_communes

	CODGEO	LIBGEO	MED14	nombre_batiments_historique	nombre_hopitaux	nombre_crime	nombre_ecole	Unnamed: 0_x	P21_POP	P21_POP0014	...	Dynamique Démographique INSEE	Capacité Fiscale	Nb Education, santé, action sociale	Nb Services personnels et domestiques	Nb Santé, action sociale	Nb de Commerce	Nb de Services aux particuliers	Nb institution de Education, santé, action sociale, administration	Score Croissance Population	Score Croissance Entrepreneuriale
0	1001	L'Abergement-Clémenciat	21576.7	1	0	112	1	0.0	832.0	158.552045	...	-1.0	117.0	3.0	1.0	0.0	9350.0	3372.0	15105.0	72.13115	0.01585
1	1002	L'Abergement-de-Varey	21672.9	0	0	112	0	1.0	267.0	57.046286	...	0.0	110.0	0.0	0.0	0.0	9350.0	3372.0	15105.0	72.13115	0.00173
2	1004	Ambérieu-en-Bugey	19756.1	2	8	112	11	2.0	14854.0	2789.758194	...	-55.0	250.0	113.0	41.0	118.0	9350.0	3372.0	15105.0	72.95082	0.38471
3	1005	Ambérieux-en-Dombes	23204.8	1	0	112	1	3.0	1897.0	367.334805	...	-3.0	127.0	5.0	2.0	7.0	9350.0	3372.0	15105.0	72.13115	0.02824
4	1006	Ambléon	22157.5	0	0	112	0	4.0	113.0	9.912281	...	0.0	109.0	0.0	0.0	0.0	9350.0	3372.0	15105.0	67.62295	0.00000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36616	97420	Sainte-Suzanne	14125.6	4	0	112	22	34958.0	24293.0	5564.329899	...	-265.0	194.0	69.0	13.0	66.0	574.0	117.0	1102.0	76.22951	0.43688
36617	97421	Salazie	10827.4	6	1	112	10	34959.0	7243.0	1700.208360	...	-117.0	104.0	25.0	1.0	29.0	574.0	117.0	1102.0	76.63934	0.13083
36618	97422	Le Tampon	13349.4	3	6	112	59	34960.0	81943.0	18285.741483	...	-734.0	-512.0	334.0	116.0	301.0	574.0	117.0	1102.0	75.40984	2.09129
36619	97423	Les Trois-Bassins	13104.5	0	0	112	10	34961.0	6899.0	1342.305384	...	-88.0	102.0	32.0	5.0	30.0	574.0	117.0	1102.0	75.81967	0.10547
36620	97424	Cilaos	11545.0	1	1	112	6	34962.0	5390.0	1082.145083	...	-81.0	95.0	22.0	5.0	29.0	574.0	117.0	1102.0	75.81967	0.08098

36621 rows × 78 columns

And here are its columns :

Know let’s check the amount of null values in our datasets.

# Vérification des valeurs manquantes
missing_values = df_communes.isnull()

# Afficher un résumé du nombre de valeurs NaN par colonne
print("Résumé des NaN par colonne :")
print(df_communes.isnull().sum())

Résumé des NaN par colonne :
CODGEO                                                                   0
LIBGEO                                                                   0
MED14                                                                 3647
nombre_batiments_historique                                              0
nombre_hopitaux                                                          0
                                                                      ... 
Nb de Commerce                                                          57
Nb de Services aux particuliers                                         57
Nb institution de Education, santé, action sociale, administration      57
Score Croissance Population                                             57
Score Croissance Entrepreneuriale                                       58
Length: 78, dtype: int64

df_communes.to_csv("df_communes_avec_Nan.csv", index=False)

Pre-Processing of the mergged dataset

df = pd.read_csv("df_communes_avec_Nan.csv")
df.isnull().sum()

<ipython-input-11-8d6e47f56637>:1: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv("df_communes_avec_Nan.csv")

	0
CODGEO	0
LIBGEO	0
MED14	3647
nombre_batiments_historique	0
nombre_hopitaux	0
...	...
Nb de Commerce	57
Nb de Services aux particuliers	57
Nb institution de Education, santé, action sociale, administration	57
Score Croissance Population	57
Score Croissance Entrepreneuriale	58

78 rows × 1 columns

dtype: int64

#Identifing numercial columns and replacing them with the mean of the column
numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns
numeric_imputer = SimpleImputer(strategy='mean')
df[numerical_columns] = numeric_imputer.fit_transform(df[numerical_columns])

#verifing my results
pd.set_option('display.max_rows', None)
print(df.isnull().sum())

CODGEO                                                                0
LIBGEO                                                                0
MED14                                                                 0
nombre_batiments_historique                                           0
nombre_hopitaux                                                       0
nombre_crime                                                          0
nombre_ecole                                                          0
Unnamed: 0_x                                                          0
P21_POP                                                               0
P21_POP0014                                                           0
P21_POP1529                                                           0
P21_POP3044                                                           0
P21_POP4559                                                           0
P21_POP6074                                                           0
P21_POP7589                                                           0
P21_POP90P                                                            0
Unnamed: 0_y                                                          0
P21_POP1564                                                           0
P21_POP1524                                                           0
P21_POP2554                                                           0
P21_CHOM1564                                                          0
P21_CHOM1524                                                          0
P21_CHOM2554                                                          0
nombre_cinemas                                                        0
nombre_musees                                                         0
nombre_festivals                                                      0
Unnamed: 0                                                            0
Annee                                                                 0
Nb_mutations                                                          0
NbMaisons                                                             0
NbApparts                                                             0
propmaison                                                            0
propappart                                                            0
PrixMoyen                                                             0
Prixm2Moyen                                                           0
SurfaceMoy                                                            0
nombre_gare                                                           0
Dynamique Entrepreneuriale                                            0
Dynamique Entrepreneuriale Service et Commerce                        0
Synergie Médicale COMMUNE                                             0
Indice Synergie Médicale                                              0
Nb Omnipraticiens BV                                                  0
Nb Infirmiers Libéraux BV                                             0
Nb dentistes Libéraux BV                                              0
Nb pharmaciens Libéraux BV                                            0
Densité Médicale BV                                                   0
Indice Démographique                                                  0
Indice Ménages                                                        0
Evolution Pop %                                                       0
Nb Ménages                                                            0
Nb Résidences Principales                                             0
Nb propriétaire                                                       0
Nb Logement                                                           0
Nb Résidences Secondaires                                             0
Nb Log Vacants                                                        0
Nb Entreprises Secteur Services                                       0
Nb Entreprises Secteur Commerce                                       0
Nb Entreprises Secteur Construction                                   0
Nb Entreprises Secteur Industrie                                      0
Nb Création Enteprises                                                0
Nb Création Industrielles                                             0
Nb Création Construction                                              0
Nb Création Commerces                                                 0
Nb Création Services                                                  0
Nb Logement Secondaire et Occasionnel                                 0
Nb Hotel                                                              0
Nb Camping                                                            0
Taux Propriété                                                        0
Dynamique Démographique INSEE                                         0
Capacité Fiscale                                                      0
Nb Education, santé, action sociale                                   0
Nb Services personnels et domestiques                                 0
Nb Santé, action sociale                                              0
Nb de Commerce                                                        0
Nb de Services aux particuliers                                       0
Nb institution de Education, santé, action sociale, administration    0
Score Croissance Population                                           0
Score Croissance Entrepreneuriale                                     0
dtype: int64

print(f"Number of rows: {df.shape[0]}")
print(f"Number of columns: {df.shape[1]}")

Number of rows: 36621
Number of columns: 78

#This shows that we don't have any duplicates in our dataset
df = df.drop_duplicates()
print(f"Number of rows: {df.shape[0]}")
print(f"Number of columns: {df.shape[1]}")

Number of rows: 36621
Number of columns: 78

#Keeping the columns that are usefull
#We are going to remove nombre_crime since they are all at 112
#We are going to drop dynamique démographique INSEE since it has negative values
#And other random columns that we're just added because of the mergging
df = df.drop(columns=['nombre_crime', 'Dynamique Démographique INSEE','Unnamed: 0', 'Unnamed: 0_x','Unnamed: 0_y'])

#Checking outliers
numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns
plt.figure(figsize=(15, len(numerical_columns) * 2))  # Adjust figure size
for i, column in enumerate(numerical_columns, 1):
    plt.subplot(len(numerical_columns), 1, i)
    plt.boxplot(df[column].dropna(), vert=False, patch_artist=True)
    plt.title(column, fontsize=10)
    plt.xlabel('Values')

plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-28-38828b71ada9> in <cell line: 11>()
      9 
     10 plt.tight_layout()
---> 11 plt.show()

/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py in show(*args, **kwargs)
    525     """
    526     _warn_if_gui_out_of_main_thread()
--> 527     return _get_backend_mod().show(*args, **kwargs)
    528 
    529 

/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py in show(close, block)
     88     try:
     89         for figure_manager in Gcf.get_all_fig_managers():
---> 90             display(
     91                 figure_manager.canvas.figure,
     92                 metadata=_fetch_figure_metadata(figure_manager.canvas.figure)

/usr/local/lib/python3.10/dist-packages/IPython/core/display.py in display(include, exclude, metadata, transient, display_id, *objs, **kwargs)
    318             publish_display_data(data=obj, metadata=metadata, **kwargs)
    319         else:
--> 320             format_dict, md_dict = format(obj, include=include, exclude=exclude)
    321             if not format_dict:
    322                 # nothing to display (e.g. _ipython_display_ took over)

/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
    178             md = None
    179             try:
--> 180                 data = formatter(obj)
    181             except:
    182                 # FIXME: log the exception

<decorator-gen-2> in __call__(self, obj)

/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
    222     """show traceback on failed format call"""
    223     try:
--> 224         r = method(self, *args, **kwargs)
    225     except NotImplementedError:
    226         # don't warn on NotImplementedErrors

/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    149         FigureCanvasBase(fig)
    150 
--> 151     fig.canvas.print_figure(bytes_io, **kw)
    152     data = bytes_io.getvalue()
    153     if fmt == 'svg':

/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2156                 # so that we can inject the orientation
   2157                 with getattr(renderer, "_draw_disabled", nullcontext)():
-> 2158                     self.figure.draw(renderer)
   2159             if bbox_inches:
   2160                 if bbox_inches == "tight":

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     93     @wraps(draw)
     94     def draw_wrapper(artist, renderer, *args, **kwargs):
---> 95         result = draw(artist, renderer, *args, **kwargs)
     96         if renderer._rasterizing:
     97             renderer.stop_rasterizing()

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer)
     70                 renderer.start_filter()
     71 
---> 72             return draw(artist, renderer)
     73         finally:
     74             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py in draw(self, renderer)
   3152 
   3153                 self.patch.draw(renderer)
-> 3154                 mimage._draw_list_compositing_images(
   3155                     renderer, self, artists, self.suppressComposite)
   3156 

/usr/local/lib/python3.10/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    130     if not_composite or not has_images:
    131         for a in artists:
--> 132             a.draw(renderer)
    133     else:
    134         # Composite any adjacent images together

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer)
     70                 renderer.start_filter()
     71 
---> 72             return draw(artist, renderer)
     73         finally:
     74             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py in draw(self, renderer)
   3068             _draw_rasterized(self.figure, artists_rasterized, renderer)
   3069 
-> 3070         mimage._draw_list_compositing_images(
   3071             renderer, self, artists, self.figure.suppressComposite)
   3072 

/usr/local/lib/python3.10/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    130     if not_composite or not has_images:
    131         for a in artists:
--> 132             a.draw(renderer)
    133     else:
    134         # Composite any adjacent images together

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer)
     70                 renderer.start_filter()
     71 
---> 72             return draw(artist, renderer)
     73         finally:
     74             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py in draw(self, renderer, *args, **kwargs)
   1389 
   1390         for tick in ticks_to_draw:
-> 1391             tick.draw(renderer)
   1392 
   1393         # Shift label away from axes to avoid overlapping ticklabels.

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer)
     70                 renderer.start_filter()
     71 
---> 72             return draw(artist, renderer)
     73         finally:
     74             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py in draw(self, renderer)
    293         for artist in [self.gridline, self.tick1line, self.tick2line,
    294                        self.label1, self.label2]:
--> 295             artist.draw(renderer)
    296         renderer.close_group(self.__name__)
    297         self.stale = False

/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer)
     70                 renderer.start_filter()
     71 
---> 72             return draw(artist, renderer)
     73         finally:
     74             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.10/dist-packages/matplotlib/text.py in draw(self, renderer)
    766 
    767             gc = renderer.new_gc()
--> 768             gc.set_foreground(self.get_color())
    769             gc.set_alpha(self.get_alpha())
    770             gc.set_url(self._url)

/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py in set_foreground(self, fg, isRGBA)
    967         self._dashes = dash_offset, dash_list
    968 
--> 969     def set_foreground(self, fg, isRGBA=False):
    970         """
    971         Set the foreground color.

KeyboardInterrupt: 

We can see that we have a lot of outliers and their behavior is changing between every column, we can't treat each column apart since the dataset is huge so we're gonna be keeping them.

From now on, you can implement only the "df_communes_avec_Nan"

Machine Learning

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

Here we implement a few graphs to get a good look at our data and start to see the links we can make between our columns.

# Graphique 1 : Répartition des prix moyens au m²
plt.figure(figsize=(10, 6))
plt.hist(df['Prixm2Moyen'], bins=15, alpha=0.7, edgecolor='black')
plt.title('Distribution des Prix Moyens au m²')
plt.xlabel('Prix Moyen au m²')
plt.ylabel('Fréquence')
plt.show()

# Graphique 2 : Corrélation entre Prixm2Moyen et Evolution Pop %
plt.figure(figsize=(10, 6))
plt.scatter(df['Prixm2Moyen'], df['Evolution Pop %'], alpha=0.6)
plt.title('Relation entre Prix Moyen au m² et Evolution de la Population (%)')
plt.xlabel('Prix Moyen au m²')
plt.ylabel('Evolution Pop %')
plt.grid(True)
plt.show()

# Graphique 3 : Boxplot pour Taux Propriété par Evolution Pop % (groupé en intervalles)
df['Evolution Pop % Group'] = pd.qcut(df['Evolution Pop %'], q=4, duplicates='drop')
plt.figure(figsize=(12, 6))
df.boxplot(column='Taux Propriété', by='Evolution Pop % Group', grid=False)
plt.title('Taux de Propriété par Quartiles de l\'Evolution de la Population')
plt.suptitle('')
plt.xlabel('Quartiles Evolution Pop %')
plt.ylabel('Taux de Propriété')
plt.show()

# Graphique 4 : Heatmap des corrélations
import seaborn as sns

correlation_columns = ['Prixm2Moyen', 'Evolution Pop %', 'Taux Propriété']
correlation_matrix = df[correlation_columns].corr()

plt.figure(figsize=(8, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Heatmap des Corrélations')
plt.show()

# Graphique 5 : Relation entre le nombre de bâtiments historiques et le prix moyen
plt.figure(figsize=(10, 6))
plt.scatter(df['nombre_batiments_historique'], df['Prixm2Moyen'], alpha=0.6, c='orange')
plt.title('Relation entre Nombre de Bâtiments Historiques et Prix Moyen au m²')
plt.xlabel('Nombre de Bâtiments Historiques')
plt.ylabel('Prix Moyen au m²')
plt.grid(True)
plt.show()

<Figure size 1200x600 with 0 Axes>

Here we also removed the columns ('CODGEO', 'LIBGEO') that are not necessary for the corelation matrix since they are location indicators. We also removed ('Unnamed: 0', 'Unnamed: 0_x') which are random columns that appeared after our merging

df =df.drop(['CODGEO', 'LIBGEO'], axis = 1)

df.dtypes

	0
MED14	float64
nombre_batiments_historique	int64
nombre_hopitaux	int64
nombre_crime	int64
nombre_ecole	int64
...	...
Nb de Services aux particuliers	float64
Nb institution de Education, santé, action sociale, administration	float64
Score Croissance Population	float64
Score Croissance Entrepreneuriale	float64
Evolution Pop % Group	category

77 rows × 1 columns

dtype: object

matrice_correlation = df.corr()

# Visualisation of the corelation matrix
plt.figure(figsize=(70, 70))
sns.heatmap(matrice_correlation, annot=True, cmap='coolwarm', fmt='.2f', linewidths=.5)
plt.title('Matrice de Corrélation')
plt.show()

Let’s analyse our correlation matrix. The bright red represents strong and positive correlation (1) and dark blue represents strong and negative corelation (-1). For example let’s analyse two of the few correlations we have :

For demographic indicators :

• Strong Positive Correlation: P21_POP correlates highly with its subcategories (P21_POP0014, P21_POP1529, etc.), indicating internal consistency in population age data.
• Negative Correlation : P21_POP7589 (older population) negatively correlates with

For real estate and economic factors :

• Strong Positive Correlation : PrixMoyen and Prixm2Moyen are highly correlated, showing consistency between property prices and price per square meter. Dynamique Entrepreneuriale and Nb Entreprises Secteur Services positively correlate, reflecting business activity linked to the service sector.
• Negative Correlation: Taux Propriété negatively correlates with Nb Log Vacants, indicating areas with high homeownership have fewer vacant properties.

X = df.drop(columns=['MED14'])
y = df['MED14']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    pipeline.fit(X_train, y_train)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 2839574.67
Root Mean Squared Error (MSE): 1685.10
R-squared (R²): 0.62

=== Support Vector Regressor ===
Mean Squared Error (MSE): 6564496.68
Root Mean Squared Error (MSE): 2562.13
R-squared (R²): 0.13

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 3538584.51
Root Mean Squared Error (MSE): 1881.11
R-squared (R²): 0.53

=== forest random regressor  ===
Mean Squared Error (MSE): 2532277.58
Root Mean Squared Error (MSE): 1591.31
R-squared (R²): 0.66

We applied ML models on the same dataset, but the dataset seems to lack meaningful signal for accurate predictions. Below is the analysis:

• Gradient Boosting Regressor: Errors are relatively high (RMSE: 1699.21) and the variance explained by the model is moderate (R²: 0.62). This model struggles to achieve a strong predictive capability.

• Support Vector Regressor: The results are very poor, with RMSE: 2556.18 and an R² of only 0.13. This suggests that the model is not robust and cannot handle the data's complexity or noise.

• K-Nearest Neighbors Regressor: This model performs consistently with RMSE: 1890.50 and R²: 0.53, showing marginally better results than SVR but still falling short compared to ensemble methods.

• Random Forest Regressor: This model achieves the best results with RMSE: 1612.36 and R²: 0.65, indicating relatively better performance. However, the error values are still high and predictions remain insufficiently accurate.

For stage 2, after getting such poor results, we'll change our focus in order to try different columns to predict on to see in me get more pertinent results with our dataset.

Stage 2 :

So in this our target column is not MED14 anymore but : Prixm2Moyen, Evolution Pop %, Taux Propriété.

colonnes_a_conserver = matrice_correlation['Prixm2Moyen'][abs(matrice_correlation['Prixm2Moyen']) >= 0.3].index

#Creation of a new dataframe with only the columns we want to keep
df_filtré = df[colonnes_a_conserver]

The following code's aim is to obtain the filtered dataframe, we did it to show that we know and can clean a dataset depending on the correlated columns.

Unfortunatly, during the training most of the time the algorithms works better when we provide them with the original dataset (not filtered).

The following code is a proof of that

In this part we are training the model to predic different aspects of each town (mean price of a square meter, population evaluation, property rate) we chose to only work on these targets because after working on others in stage 1, the results where not interesting at all, the errors where too large. We think that this might be caused by weak correlatin, missing data,or uncertainty in the data quality.

We are using differents ML models (Gradient Boosting Regressor, Support Vector Regressor, K-Nearest Neighbors Regressor, forest random regressor). Some were already used in stage 1 and others were added in this stage to broaden our approach and improve performance.

We are also evaluating our preformance with (Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²)).

# Preparation of the data
X = df_filtré.drop(columns=['Prixm2Moyen'])
y = df_filtré['Prixm2Moyen']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Pipeline for the tranformation of the numerical columns
numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),  # Replace missing values by the mean
    ('scaler', StandardScaler())                 # Data normalization
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

# Alternatif models
models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

# Evaluation of each model
for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    # Model training
    pipeline.fit(X_train, y_train)

    # Predictions
    y_pred = pipeline.predict(X_test)

    # Performance evaluation
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 171193.35
Root Mean Squared Error (MSE): 413.76
R-squared (R²): 0.79

=== Support Vector Regressor ===
Mean Squared Error (MSE): 423642.60
Root Mean Squared Error (MSE): 650.88
R-squared (R²): 0.49

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 202918.08
Root Mean Squared Error (MSE): 450.46
R-squared (R²): 0.75

=== forest random regressor  ===
Mean Squared Error (MSE): 189335.29
Root Mean Squared Error (MSE): 435.13
R-squared (R²): 0.77

pipeline

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer()),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  Index(['MED14', 'propmaison', 'propappart', 'PrixMoyen', 'Nb de Commerce',
       'Nb de Services aux particuliers',
       'Nb institution de Education, santé, action sociale, administration'],
      dtype='object'))])),
                ('regressor', RandomForestRegressor(random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

• Gradient Boosting Regresso : The mean squared error is relatively low which indicates that the model preforms well. The RMSE shows an error of 438.42 euros. The R2 shows that 82% of the variance in "Prix2Moyen" is explained by the input variable. These are among the best results across all the models but we think it is not good enough since the error is 438 euros on an average price of 2000 euros.

• Support Vector Regressor : The MSE and RMSE shows that the errors are significantly higher (784 euros of difference). The predictions are far less precise compared to other models. This is possibly due to its sensitivity to hyperparameters.

• K-Nearest Neighbors Regressor : MSE and RMSE errors are relatively low close to those of Gradient Boosting. This indicates that KNN is a good performer but still it is less precise than Gradient Boosting.

• Forest random regressor : This model is as good as the gradient boosting.

Test avec PCA :

X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

pca = PCA(n_components=0.90)


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('pca', pca),
        ('regressor', model)
    ])

    pipeline.fit(X_train, y_train)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 262904.79
Root Mean Squared Error (MSE): 512.74
R-squared (R²): 0.68

=== Support Vector Regressor ===
Mean Squared Error (MSE): 549112.87
Root Mean Squared Error (MSE): 741.02
R-squared (R²): 0.34

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 264041.69
Root Mean Squared Error (MSE): 513.85
R-squared (R²): 0.68

=== forest random regressor  ===
Mean Squared Error (MSE): 219715.26
Root Mean Squared Error (MSE): 468.74
R-squared (R²): 0.73

We retried the same ML models on the same dataset to predict the real estate price per square meter by municipality, and here are the results:

• Gradient Boosting Regressor: Errors are relatively low (RMSE: 512.74), and the variance explained by the model is good (R²: 0.68). The model performs well but leaves room for improvement in prediction accuracy.

• Support Vector Regressor: The results are poor, with RMSE: 741.02 and an R² of only 0.34, indicating that the model is not well-suited for this task or struggles with the data structure.

• K-Nearest Neighbors Regressor: This model performs similarly to Gradient Boosting with RMSE: 513.85 and R²: 0.68, suggesting it captures the data patterns reasonably well.

• Random Forest Regressor: This model achieves the best results with RMSE: 468.74 and R²: 0.73, indicating a strong predictive capability and high accuracy compared to the other models.

X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    pipeline.fit(X_train, y_train)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 29051.79
Root Mean Squared Error (MSE): 170.45
R-squared (R²): 0.96

=== Support Vector Regressor ===
Mean Squared Error (MSE): 479927.02
Root Mean Squared Error (MSE): 692.77
R-squared (R²): 0.42

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 159837.44
Root Mean Squared Error (MSE): 399.80
R-squared (R²): 0.81

=== forest random regressor  ===
Mean Squared Error (MSE): 29861.43
Root Mean Squared Error (MSE): 172.80
R-squared (R²): 0.96

We retried the same ML models on the same dataset, but this time the dataset was not filtered by correlation. And we have much better results (we don't understand why, maybe we lost a lot of important data after filtering).

• Gradient Boosting Regressor : Errors are significaly lower than before (170,50 euros) and predicitons are much more accurate (96% of the variance is explained in the target variable). Gradient boosting benefits significantly from the additional information in the unfiltered dataset

• Support Vector Regressor : The results are even worse than before this model is performing very poorly. This could inidcate that the model is not robust to noisy or uncorrelated data.

• K-Nearest Neighbors Regressor : KNN is performing the same way as before it is relatively stable. We can see a marginal improvement. However, Gradient Boosting and Random Forest are still much more accurate.

• forest random regressor : Massive improvment with an error of 173.71 RMSE compared to 468.74 before. And an R2 of 0.96 indicated highly accurate results. Like gradient boosting this model benefits significantly from the unfiltered datasets using the adidtional data to improve predictions.

y

	Prixm2Moyen
0	2249.500000
1	1913.500000
2	2399.620253
3	3287.962963
4	1975.924278
5	2274.871795
6	2538.800000
7	1862.666667
8	2795.944444
9	2210.800000
10	1925.375000
11	1006.500000
12	1763.600000
13	1926.555556
14	2274.000000
15	1388.333333
16	1975.924278
17	4077.333333
18	2069.823529
19	1845.000000
20	2348.793103
21	2112.090909
22	2300.333333
23	3153.400000
24	2622.500000
25	1778.142857
26	2287.000000
27	1913.384615
28	3371.095238
29	2444.852381
30	2048.744681
31	2369.000000
32	2036.800000
33	2470.750000
34	1586.000000
35	1396.000000
36	1399.666667
37	2167.142857
38	2939.500000
39	3926.644444
40	2707.750000
41	2232.500000
42	2275.846154
43	2989.500000
44	3244.142857
45	1763.500000
46	1127.000000
47	1885.000000
48	2258.120805
49	2817.142857
50	1400.333333
51	2238.666667
52	2240.545455
53	1975.924278
54	1975.924278
55	2430.357143
56	3683.200000
57	1691.000000
58	2087.363636
59	2491.437500
60	1713.000000
61	1701.000000
62	1235.500000
63	2236.357143
64	4799.765625
65	2501.184211
66	1968.500000
67	2609.647059
68	3188.437500
69	702.000000
70	1463.333333
71	4764.625000
72	2100.750000
73	1922.000000
74	2087.875000
75	2249.750000
76	3029.800000
77	1882.571429
78	1420.000000
79	1307.333333
80	3323.181818
81	2335.052632
82	1647.750000
83	1975.924278
84	2411.642857
85	2890.372881
86	2200.000000
87	1304.333333
88	2597.142857
89	1975.924278
90	2097.166667
91	3086.615385
92	842.000000
93	2541.000000
94	2333.272727
95	4689.956522
96	2234.000000
97	3830.866667
98	2495.500000
99	1191.000000
100	1552.300000
101	3944.815789
102	1860.000000
103	1996.166667
104	2209.857143
105	1526.400000
106	2754.142857
107	2102.181818
108	1917.833333
109	2015.500000
110	2760.000000
111	1975.924278
112	538.000000
113	1975.924278
114	2553.923077
115	1882.272727
116	1343.000000
117	1634.777778
118	1790.500000
119	3353.000000
120	2041.777778
121	2414.000000
122	2281.461538
123	4990.100000
124	2524.142857
125	2053.685714
126	2078.200000
127	2260.750000
128	1951.600000
129	3417.382979
130	5986.084967
131	1975.924278
132	2554.777778
133	2771.000000
134	2182.333333
135	2148.000000
136	2481.571429
137	1706.666667
138	2551.785714
139	3313.500000
140	4787.931034
141	1975.924278
142	1424.666667
143	3708.571429
144	3050.333333
145	4074.066667
146	2157.968750
147	4744.959732
148	1975.924278
149	2090.500000
150	3111.636364
151	2925.893617
152	2598.666667
153	2470.250000
154	2829.500000
155	1916.833333
156	1975.924278
157	4410.769585
158	2347.000000
159	1819.000000
160	1975.924278
161	1080.000000
162	2381.315789
163	6125.777778
164	857.000000
165	1975.924278
166	2614.631579
167	2519.250000
168	1741.079365
169	1975.924278
170	2298.333333
171	3425.250000
172	2998.000000
173	1381.500000
174	1262.500000
175	1912.800000
176	2344.000000
177	3153.333333
178	2476.187500
179	1866.285714
180	2218.750000
181	1975.924278
182	1852.208333
183	1424.500000
184	2444.561644
185	2630.818182
186	2250.833333
187	1975.924278
188	1216.500000
189	2066.666667
190	1762.000000
191	3686.666667
192	2633.434783
193	2112.642857
194	1676.333333
195	2667.500000
196	1075.500000
197	1865.000000
198	1956.888889
199	1975.924278
200	1771.333333
201	1975.924278
202	2821.238095
203	4196.750000
204	2098.500000
205	2196.400000
206	3354.400000
207	1390.666667
208	2094.181818
209	1811.458333
210	1533.666667
211	2586.500000
212	2757.071429
213	1800.100000
214	1963.000000
215	3118.769231
216	2672.200000
217	1709.363636
218	2004.818182
219	895.000000
220	3765.300000
221	3002.324675
222	1471.333333
223	2143.470588
224	2348.142857
225	3998.307692
226	3547.887931
227	3257.166667
228	3085.222222
229	3100.875000
230	1593.000000
231	3469.666667
232	2433.800000
233	2247.000000
234	3305.750000
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1944	5774.170213
1945	4199.875000
1946	2423.157895
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1953	4856.146771
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1956	4291.859419
1957	3590.357143
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1965	4932.250000
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1975	1975.924278
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1983	2536.166667
1984	3369.222222
1985	9693.910714
1986	7241.666667
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1988	857.800000
1989	2227.500000
1990	4778.290323
1991	5151.734694
1992	3413.388889
1993	4850.071429
1994	3685.600000
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2010	5599.407080
2011	6349.124224
2012	2745.363636
2013	1975.924278
2014	5014.191147
2015	7662.619048
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4582	4292.625000
4583	1975.924278
4584	2304.583333
4585	1975.924278
4586	6117.514286
4587	2685.062500
4588	1453.190476
4589	602.000000
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4592	1975.924278
4593	3560.357143
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4596	2536.500000
4597	1975.924278
4598	1975.924278
4599	1975.924278
4600	2653.333333
4601	3357.909091
4602	1962.750000
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4604	1015.000000
4605	1975.924278
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4608	1425.833333
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4610	3916.500000
4611	1975.924278
4612	2065.000000
4613	2800.666667
4614	1955.545455
4615	3093.250000
4616	2718.769231
4617	4369.666667
4618	2044.000000
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4620	2476.014706
4621	1975.924278
4622	2435.571429
4623	3120.645833
4624	2759.153846
4625	2470.100000
4626	2906.500000
4627	1975.924278
4628	2936.600000
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4630	1975.924278
4631	2142.000000
4632	2094.500000
4633	1975.924278
4634	1975.924278
4635	2055.750000
4636	5389.721264
4637	3046.824463
4638	1862.419355
4639	1602.357143
4640	1556.000000
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4647	1975.924278
4648	1975.924278
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4650	3596.166667
4651	1975.924278
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4654	1975.924278
4655	2981.714286
4656	1089.666667
4657	1975.924278
4658	1996.833333
4659	1975.924278
4660	1975.924278
4661	1426.628571
4662	1975.924278
4663	1671.000000
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4665	1602.000000
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4667	1917.000000
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4669	1975.924278
4670	1975.924278
4671	1975.924278
4672	1975.924278
4673	1975.924278
4674	1975.924278
4675	1975.924278
4676	1975.924278
4677	1975.924278
4678	1975.924278
4679	6040.750000
4680	1662.375000
4681	2005.000000
4682	1975.924278
4683	2483.333333
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4685	2777.347826
4686	1686.666667
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4688	1975.924278
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4693	1208.000000
4694	1975.924278
4695	2143.222222
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4697	2111.000000
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4699	1733.111111
4700	1084.000000
4701	1656.333333
4702	1975.924278
4703	1975.924278
4704	1975.924278
4705	1975.924278
4706	1975.924278
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4709	1975.924278
4710	2031.833333
4711	1883.727273
4712	1584.750000
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4714	2769.400000
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4717	1975.924278
4718	4785.333333
4719	2860.200000
4720	1975.924278
4721	2655.750000
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4724	1975.924278
4725	1700.333333
4726	1975.924278
4727	2663.000000
4728	1975.924278
4729	1975.924278
4730	2482.000000
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4733	1975.924278
4734	2769.000000
4735	1975.924278
4736	7011.112319
4737	2523.942857
4738	1975.924278
4739	1975.924278
4740	3404.000000
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4742	1194.250000
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4744	3108.750000
4745	2312.555556
4746	1975.924278
4747	2880.857143
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4749	1975.924278
4750	2860.250000
4751	2236.428571
4752	6269.000000
4753	2092.000000
4754	1873.500000
4755	1719.400000
4756	3042.111111
4757	3090.176471
4758	1993.000000
4759	1743.500000
4760	2338.000000
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4762	2444.777778
4763	1975.924278
4764	1275.666667
4765	1754.250000
4766	1975.924278
4767	2803.285714
4768	1975.924278
4769	2446.500000
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4771	1908.065041
4772	1975.924278
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4775	1975.924278
4776	1975.924278
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4778	1975.924278
4779	2558.333333
4780	2350.200000
4781	3038.488372
4782	1016.000000
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4789	1975.924278
4790	2738.333333
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4795	1975.924278
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4800	2129.400000
4801	1715.500000
4802	1975.924278
4803	1738.500000
4804	1975.924278
4805	1975.924278
4806	2275.400000
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4809	1975.924278
4810	2733.414634
4811	1975.924278
4812	1868.333333
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4815	2753.300000
4816	1975.924278
4817	2865.166667
4818	2057.727273
4819	2182.428571
4820	2111.750000
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4822	1975.924278
4823	5490.500000
4824	1975.924278
4825	2850.000000
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4827	1510.666667
4828	2623.000000
4829	1975.924278
4830	3084.140351
4831	2637.714286
4832	2255.660584
4833	2926.800000
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4835	1975.924278
4836	1975.924278
4837	1623.800000
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4839	2029.500000
4840	2520.600000
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4843	4479.779006
4844	1975.924278
4845	2538.091549
4846	1363.927273
4847	1975.924278
4848	2305.666667
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4851	1750.333333
4852	1975.924278
4853	2148.250000
4854	1975.924278
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4859	1975.924278
4860	1465.300000
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4862	1975.924278
4863	1975.924278
4864	1975.924278
4865	2929.000000
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4868	1911.097315
4869	1439.833333
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4871	1975.924278
4872	1877.538462
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4874	1975.924278
4875	1975.924278
4876	1975.924278
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4878	1975.924278
4879	2345.857143
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4881	2146.666667
4882	1923.500000
4883	1975.924278
4884	1975.924278
4885	3087.714286
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4887	1975.924278
4888	1975.924278
4889	1975.924278
4890	1975.924278
4891	1083.000000
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4896	1975.924278
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4900	4007.666667
4901	2231.000000
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4904	2387.666667
4905	1981.500000
4906	1873.500000
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4908	3308.448276
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4910	4239.922078
4911	1870.538462
4912	2031.500000
4913	1975.924278
4914	1975.924278
4915	1975.924278
4916	1975.924278
4917	1975.924278
4918	1975.924278
4919	1975.924278
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4921	1975.924278
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4923	1975.924278
4924	1975.924278
4925	1220.000000
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4927	1975.924278
4928	1755.250000
4929	1975.924278
4930	1302.500000
4931	2007.160377
4932	1975.924278
4933	1975.924278
4934	1835.000000
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4939	1975.924278
4940	1975.924278
4941	1975.924278
4942	1975.924278
4943	1975.924278
4944	1040.333333
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4946	1975.924278
4947	2336.500000
4948	1975.924278
4949	1250.571429
4950	1651.200000
4951	2171.733333
4952	1768.142857
4953	2429.148148
4954	2023.500000
4955	2097.000000
4956	1975.924278
4957	1992.083333
4958	2153.750000
4959	1975.924278
4960	2066.500000
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4962	725.000000
4963	1802.250000
4964	742.500000
4965	1975.924278
4966	2196.000000
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4968	1994.677419
4969	2181.500000
4970	1975.924278
4971	1471.161290
4972	1778.000000
4973	1975.924278
4974	2834.333333
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4980	1975.924278
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4983	1975.924278
4984	3108.500000
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4991	1409.500000
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4993	1975.924278
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5000	1079.666667
5001	1975.924278
5002	1975.924278
5003	2989.962963
5004	2464.147059
5005	1883.571429
5006	1975.924278
5007	1975.924278
5008	2261.000000
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5012	1975.924278
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5014	1975.924278
5015	2012.833333
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5020	1975.924278
5021	1975.924278
5022	2243.000000
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5025	2606.411765
5026	1975.924278
5027	3010.750000
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5031	1975.924278
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5035	1975.924278
5036	1975.924278
5037	1975.924278
5038	1975.924278
5039	1894.857143
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5041	2405.166667
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5048	2693.500000
5049	1975.924278
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5052	1975.924278
5053	2943.000000
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5057	1975.924278
5058	1690.416667
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5061	1975.924278
5062	3727.200000
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5064	1975.924278
5065	1975.924278
5066	2027.833333
5067	1975.924278
5068	1975.924278
5069	1975.924278
5070	2055.000000
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5079	1975.924278
5080	1975.924278
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5082	1975.924278
5083	1975.924278
5084	2344.000000
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5086	1851.714286
5087	1975.924278
5088	3410.857143
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5090	1961.500000
5091	1975.924278
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5094	1975.924278
5095	1975.924278
5096	1790.500000
5097	1975.924278
5098	1975.924278
5099	1975.924278
5100	1975.924278
5101	3160.500000
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5108	1975.924278
5109	1975.924278
5110	2700.333333
5111	1975.924278
5112	1975.924278
5113	1975.924278
5114	882.500000
5115	1975.924278
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5117	1975.924278
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5119	2020.666667
5120	2146.000000
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5124	1975.924278
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5128	3426.333333
5129	1975.924278
5130	1975.924278
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5132	1975.924278
5133	1543.600000
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5139	1975.924278
5140	2455.323529
5141	1508.222222
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5144	1975.924278
5145	1990.000000
5146	1739.333333
5147	1975.924278
5148	1975.924278
5149	1610.181818
5150	1975.924278
5151	2700.066667
5152	2286.307692
5153	1973.000000
5154	2441.571429
5155	2421.500000
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5160	2773.695652
5161	1975.924278
5162	1975.924278
5163	1975.924278
5164	1964.692308
5165	1975.924278
5166	1975.924278
5167	2370.086957
5168	1975.924278
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5170	1975.924278
5171	1975.924278
5172	1975.924278
5173	2498.000000
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5176	5594.442623
5177	1975.924278
5178	1975.924278
5179	1975.924278
5180	1018.333333
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5185	1975.924278
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5192	1975.924278
5193	1975.924278
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5197	1975.924278
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5201	1975.924278
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5203	1975.924278
5204	1975.924278
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5210	1928.800000
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5213	1722.500000
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5215	1975.924278
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5219	1975.924278
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5222	1975.924278
5223	1975.924278
5224	1844.875000
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5230	1975.924278
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5232	897.000000
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5236	1975.924278
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5244	1975.924278
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5257	1975.924278
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5267	1975.924278
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5270	1975.924278
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5276	1975.924278
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5278	1975.924278
5279	1975.924278
5280	1005.230769
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5282	1975.924278
5283	1975.924278
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5293	1975.924278
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5295	1975.924278
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5299	1975.924278
5300	1227.000000
5301	1975.924278
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5312	1975.924278
5313	1064.142857
5314	768.500000
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5316	1195.750000
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5325	893.000000
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5330	1050.000000
5331	1975.924278
5332	701.000000
5333	1975.924278
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5340	1078.000000
5341	1975.924278
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5350	2050.625000
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5353	1975.924278
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5357	1975.924278
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5361	1975.924278
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5364	997.000000
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5368	1975.924278
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5371	1975.924278
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5377	1975.924278
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5380	1137.500000
5381	1975.924278
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5390	1975.924278
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5400	1815.750000
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5403	1975.924278
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5409	1975.924278
5410	2236.500000
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5412	1999.000000
5413	1863.666667
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5420	1450.250000
5421	1975.924278
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5425	1975.924278
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5427	1975.924278
5428	1975.924278
5429	1975.924278
5430	1111.000000
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5434	1464.222222
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8610	1990.488889
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8614	1923.857143
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8627	1975.924278
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8631	1975.924278
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8636	1975.924278
8637	1975.924278
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8660	1975.924278
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8746	1975.924278
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8760	1975.924278
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8765	1975.924278
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8790	1975.924278
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8830	1975.924278
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8833	1249.181818
8834	2291.500000
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8838	1975.924278
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8851	1975.924278
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8854	1975.924278
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8859	2133.200000
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8883	1975.924278
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8885	1975.924278
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9021	1975.924278
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9029	1975.924278
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9033	1975.924278
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9050	1975.924278
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9072	1975.924278
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9077	1975.924278
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9119	1450.000000
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9212	1975.924278
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9216	1975.924278
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9260	1975.924278
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9399	1975.924278
9400	963.000000
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9410	1975.924278
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9426	1975.924278
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9445	1975.924278
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9465	1975.924278
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9467	1975.924278
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9524	1975.924278
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9536	1975.924278
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9545	1975.924278
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9551	1975.924278
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9553	1911.062500
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9556	1975.924278
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9579	1975.924278
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9582	1975.924278
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9593	1975.924278
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9614	1975.924278
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9629	1975.924278
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9631	1975.924278
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9635	1975.924278
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9660	2757.416667
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9662	1975.924278
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9664	1998.947020
9665	2698.500000
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9667	3023.000000
9668	1975.924278
9669	1975.924278
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9680	2411.040816
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9685	2995.714286
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9688	2877.571429
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9692	1975.924278
9693	1975.924278
9694	1975.924278
9695	3741.000000
9696	1975.924278
9697	2756.833333
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9699	2374.600000
9700	3482.562500
9701	2808.166667
9702	2933.000000
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9705	2091.402697
9706	2400.000000
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9709	4570.500000
9710	4852.000000
9711	3192.750000
9712	3286.666667
9713	2584.083333
9714	2163.312500
9715	2309.000000
9716	2147.333333
9717	3156.750000
9718	3131.833333
9719	2005.235294
9720	5221.000000
9721	1975.924278
9722	1864.750000
9723	1975.924278
9724	1975.924278
9725	2467.425532
9726	2105.400000
9727	1584.000000
9728	2423.000000
9729	2641.058824
9730	1320.500000
9731	2082.157895
9732	2599.000000
9733	1375.000000
9734	1975.924278
9735	1840.100000
9736	2556.666667
9737	2720.826087
9738	5611.000000
9739	2270.285714
9740	2309.000000
9741	2009.909091
9742	3222.000000
9743	2191.000000
9744	2023.727273
9745	1968.750000
9746	2511.875000
9747	2458.600000
9748	2327.380435
9749	2108.818182
9750	3189.090909
9751	1806.000000
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9755	2774.000000
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9757	1430.666667
9758	4103.500000
9759	2428.333333
9760	5004.600000
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9763	2327.666667
9764	1607.600000
9765	1541.500000
9766	2885.000000
9767	791.000000
9768	5214.000000
9769	2187.800000
9770	2865.000000
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9774	750.000000
9775	5278.000000
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9777	2050.933333
9778	1975.924278
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9780	2433.000000
9781	3014.826087
9782	2352.666667
9783	1975.924278
9784	3988.666667
9785	2120.750000
9786	2283.427529
9787	1650.000000
9788	5638.333333
9789	2995.000000
9790	1975.924278
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9793	1975.924278
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9795	1975.924278
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9798	1975.924278
9799	1975.924278
9800	5060.000000
9801	3593.100000
9802	1975.924278
9803	2102.500000
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9806	2112.285714
9807	1448.400000
9808	1939.666667
9809	2177.736842
9810	1134.000000
9811	1985.200000
9812	2405.833333
9813	1975.924278
9814	2436.923077
9815	1709.900000
9816	3536.000000
9817	2443.333333
9818	1777.800000
9819	1689.333333
9820	2026.800000
9821	2541.583333
9822	1910.345679
9823	1992.266667
9824	2085.357143
9825	2148.250000
9826	2016.631579
9827	2197.250000
9828	2154.166667
9829	2399.200000
9830	1975.924278
9831	2328.666667
9832	1990.000000
9833	2095.000000
9834	1473.428571
9835	2141.750000
9836	1719.714286
9837	1934.000000
9838	2667.000000
9839	1064.000000
9840	2066.200000
9841	1842.800000
9842	2130.500000
9843	1596.500000
9844	1448.250000
9845	1975.924278
9846	1975.924278
9847	1857.071429
9848	1987.727273
9849	2254.625000
9850	2170.600000
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9854	1824.777778
9855	1694.090909
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9857	1975.924278
9858	1780.200000
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9860	2084.582734
9861	2357.333333
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9864	1975.924278
9865	1689.142857
9866	1980.782609
9867	1905.666667
9868	1884.076923
9869	2190.244186
9870	2592.000000
9871	2377.222222
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9875	1975.924278
9876	1930.666667
9877	1849.473684
9878	1701.000000
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9880	2629.600000
9881	2194.444444
9882	1992.000000
9883	1738.000000
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9886	1927.250000
9887	1975.924278
9888	1975.928571
9889	1975.924278
9890	1835.000000
9891	1985.861111
9892	2742.125000
9893	1975.924278
9894	1975.924278
9895	2867.000000
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9898	2593.428571
9899	1951.500000
9900	2329.000000
9901	2256.727273
9902	2216.333333
9903	1904.545455
9904	2440.194444
9905	1443.750000
9906	2209.810811
9907	2330.181818
9908	1682.375000
9909	1673.000000
9910	2143.333333
9911	1964.285714
9912	3096.600000
9913	1626.982143
9914	1975.924278
9915	2264.928571
9916	2457.000000
9917	1600.423077
9918	1478.928571
9919	2322.750000
9920	2559.041667
9921	1478.666667
9922	1975.924278
9923	1975.924278
9924	2694.250000
9925	2346.500000
9926	1884.800000
9927	2024.434783
9928	1920.400000
9929	1975.924278
9930	1866.200000
9931	1317.333333
9932	1975.924278
9933	1748.625000
9934	1984.142857
9935	1170.000000
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9937	2534.000000
9938	1103.250000
9939	1894.333333
9940	1085.666667
9941	3714.250000
9942	1975.924278
9943	2266.000000
9944	1975.924278
9945	1679.000000
9946	1975.924278
9947	1620.000000
9948	2008.875000
9949	1838.750000
9950	1647.666667
9951	1975.924278
9952	1696.500000
9953	2364.625000
9954	1947.500000
9955	2073.384615
9956	2302.000000
9957	1674.833333
9958	1699.333333
9959	2447.200000
9960	1975.924278
9961	1975.924278
9962	1788.142857
9963	1744.000000
9964	1975.924278
9965	1937.923077
9966	1819.607143
9967	1975.924278
9968	1742.833333
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9970	2387.882353
9971	2195.050000
9972	2285.750000
9973	1975.924278
9974	1627.000000
9975	1692.285714
9976	1975.924278
9977	4712.000000
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9979	2289.875000
9980	2087.500000
9981	2296.200000
9982	1975.924278
9983	2346.233333
9984	3465.000000
9985	1821.250000
9986	2146.500000
9987	2094.769231
9988	1958.333333
9989	2213.000000
9990	2062.407407
9991	2330.625000
9992	1895.000000
9993	2540.000000
9994	1975.924278
9995	2509.333333
9996	1975.924278
9997	1738.777778
9998	2618.250000
9999	2158.333333
10000	790.000000
10001	2011.625000
10002	5018.200000
10003	2523.500000
10004	1684.222222
10005	1681.500000
10006	1512.666667
10007	1742.500000
10008	1656.250000
10009	2046.125000
10010	1975.924278
10011	1975.924278
10012	2374.823529
10013	2304.833333
10014	1763.666667
10015	2049.800000
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10018	1791.166667
10019	2256.750000
10020	1975.924278
10021	1668.500000
10022	1975.924278
10023	1748.166667
10024	1975.924278
10025	1992.358974
10026	1938.333333
10027	1844.000000
10028	2013.172185
10029	2255.814815
10030	2682.800000
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10033	2119.000000
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10035	1807.500000
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10037	2092.428571
10038	2096.666667
10039	1707.333333
10040	1786.421053
10041	3535.142857
10042	1975.924278
10043	1956.800000
10044	1787.277778
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10047	2296.000000
10048	1975.924278
10049	1858.714286
10050	1670.500000
10051	1975.924278
10052	2817.400000
10053	1975.924278
10054	1750.750000
10055	1975.924278
10056	3277.142857
10057	1541.250000
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10059	2495.333333
10060	1975.924278
10061	1918.142857
10062	1975.924278
10063	1975.924278
10064	1838.666667
10065	1649.333333
10066	1975.924278
10067	1736.333333
10068	1975.924278
10069	1975.924278
10070	2056.000000
10071	1975.924278
10072	1863.060241
10073	1504.833333
10074	2064.875000
10075	2230.473684
10076	2129.111111
10077	2089.454545
10078	2769.200000
10079	1820.750000
10080	1975.924278
10081	2553.025862
10082	3485.600000
10083	1948.000000
10084	2088.285714
10085	1702.666667
10086	2983.666667
10087	1889.714286
10088	1736.000000
10089	1975.924278
10090	1975.924278
10091	1746.388889
10092	1162.625000
10093	1975.924278
10094	1975.924278
10095	1746.571429
10096	2100.704545
10097	1582.375000
10098	2153.545455
10099	2004.277778
10100	1975.924278
10101	1975.924278
10102	1975.924278
10103	2093.156250
10104	2219.200000
10105	1975.924278
10106	2571.000000
10107	2034.142857
10108	1953.400000
10109	3061.000000
10110	2445.750000
10111	1627.750000
10112	2473.416667
10113	1676.333333
10114	1416.000000
10115	2327.200000
10116	1890.400000
10117	1975.924278
10118	2356.235294
10119	1824.750000
10120	2275.250000
10121	1975.924278
10122	2420.666667
10123	1728.500000
10124	1994.000000
10125	2623.000000
10126	1902.000000
10127	2829.750000
10128	1891.800000
10129	3768.000000
10130	2216.363636
10131	2537.800000
10132	2501.000000
10133	2042.777778
10134	2210.454545
10135	2389.833333
10136	1975.924278
10137	1975.924278
10138	2612.222222
10139	1975.924278
10140	2480.800000
10141	2485.500000
10142	2369.400000
10143	2135.312500
10144	1719.111111
10145	1890.928571
10146	2216.333333
10147	2225.333333
10148	1539.000000
10149	2322.553191
10150	1975.924278
10151	2912.000000
10152	1847.000000
10153	1996.200000
10154	2349.200000
10155	1975.924278
10156	1427.000000
10157	1405.666667
10158	1720.923077
10159	1975.924278
10160	2085.250000
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10162	1064.000000
10163	1388.200000
10164	1649.000000
10165	2239.666667
10166	2267.000000
10167	1586.750000
10168	2177.338164
10169	5132.000000
10170	2160.066667
10171	1965.333333
10172	2185.000000
10173	2475.000000
10174	2411.000000
10175	2148.666667
10176	1660.272727
10177	2567.222222
10178	1945.785714
10179	1894.642857
10180	1975.924278
10181	1943.111111
10182	1950.666667
10183	1977.000000
10184	2005.500000
10185	1985.666667
10186	2457.833333
10187	1744.200000
10188	1735.500000
10189	1692.200000
10190	2496.666667
10191	2022.636364
10192	1800.500000
10193	2712.666667
10194	1661.500000
10195	1975.924278
10196	1625.000000
10197	1579.000000
10198	2614.000000
10199	2537.941176
10200	2101.000000
10201	2752.285714
10202	2973.333333
10203	1961.333333
10204	2127.666667
10205	2140.777778
10206	2117.222222
10207	2870.333333
10208	1975.924278
10209	2174.083333
10210	1975.924278
10211	1611.000000
10212	3522.000000
10213	2215.833333
10214	2685.750000
10215	3034.111111
10216	1341.000000
10217	1658.740741
10218	2452.666667
10219	1249.000000
10220	2260.409091
10221	1785.000000
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10224	2063.714286
10225	1151.666667
10226	1975.924278
10227	2239.600000
10228	1953.000000
10229	1750.666667
10230	1886.869565
10231	2607.333333
10232	2186.875000
10233	1975.924278
10234	1599.000000
10235	2147.250000
10236	2538.500000
10237	1794.333333
10238	1918.500000
10239	2836.326531
10240	1975.924278
10241	2062.000000
10242	1975.924278
10243	1587.055556
10244	1808.250000
10245	2357.000000
10246	2191.833333
10247	1865.454545
10248	2008.230769
10249	1975.924278
10250	1820.000000
10251	409.000000
10252	1972.909091
10253	2035.250000
10254	2322.250000
10255	1742.571429
10256	2135.846715
10257	1692.500000
10258	2134.521739
10259	1888.615385
10260	2232.000000
10261	1975.924278
10262	2188.214286
10263	2319.562500
10264	1786.333333
10265	2700.000000
10266	2753.200000
10267	2394.571429
10268	1906.600000
10269	2286.200000
10270	1975.924278
10271	1872.333333
10272	1975.924278
10273	1328.000000
10274	2244.555556
10275	1939.166667
10276	1992.000000
10277	2200.428571
10278	1364.750000
10279	1975.924278
10280	1705.333333
10281	2059.162162
10282	2131.000000
10283	1394.700000
10284	1802.727273
10285	1736.500000
10286	1975.924278
10287	2302.416667
10288	2697.000000
10289	1381.620690
10290	1975.924278
10291	2023.363636
10292	1374.000000
10293	1975.924278
10294	2198.470588
10295	1432.250000
10296	1975.924278
10297	1793.666667
10298	2899.333333
10299	1975.924278
10300	1842.571429
10301	1975.924278
10302	2167.888889
10303	2169.000000
10304	1594.375000
10305	1975.924278
10306	1184.666667
10307	610.000000
10308	1747.833333
10309	1975.924278
10310	1980.000000
10311	1718.000000
10312	1975.924278
10313	1975.924278
10314	2333.553191
10315	2458.200000
10316	1975.924278
10317	2110.666667
10318	1975.924278
10319	1925.500000
10320	2361.692308
10321	1629.000000
10322	2844.500000
10323	1956.909091
10324	2375.250000
10325	2845.000000
10326	1856.000000
10327	1975.924278
10328	1774.090909
10329	2009.444444
10330	3284.000000
10331	1989.000000
10332	1975.924278
10333	1502.950000
10334	1722.727273
10335	1975.924278
10336	1422.750000
10337	1975.924278
10338	1417.500000
10339	1369.000000
10340	2293.625000
10341	2063.000000
10342	1975.924278
10343	2107.571429
10344	1475.750000
10345	1900.000000
10346	1774.500000
10347	2344.800000
10348	1946.166667
10349	1278.000000
10350	1697.625000
10351	1975.924278
10352	2896.000000
10353	1697.571429
10354	1878.500000
10355	1929.000000
10356	2192.000000
10357	2169.000000
10358	1975.924278
10359	1975.924278
10360	2354.166667
10361	1830.555556
10362	1218.800000
10363	1232.000000
10364	2051.173913
10365	1975.924278
10366	1976.857143
10367	2432.333333
10368	1975.924278
10369	1997.750000
10370	1975.924278
10371	2175.571429
10372	1593.000000
10373	3545.500000
10374	2941.142857
10375	2250.200000
10376	1752.833333
10377	1840.615385
10378	1975.924278
10379	2103.000000
10380	2043.000000
10381	2377.750000
10382	1975.924278
10383	2327.000000
10384	1951.964912
10385	1951.666667
10386	2952.333333
10387	2218.800000
10388	2750.500000
10389	1975.924278
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10391	2874.500000
10392	1975.924278
10393	2918.000000
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10395	1890.200000
10396	4306.500000
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10398	2159.565217
10399	1779.000000
10400	1776.272727
10401	2126.600000
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10403	1628.954545
10404	2168.333333
10405	1931.181818
10406	1784.000000
10407	1975.924278
10408	3003.333333
10409	1975.924278
10410	1826.160000
10411	1975.924278
10412	1762.750000
10413	2149.500000
10414	2080.666667
10415	1975.924278
10416	2000.000000
10417	1975.924278
10418	1975.924278
10419	2215.239130
10420	1975.924278
10421	1632.750000
10422	2025.000000
10423	1975.924278
10424	1199.700000
10425	2316.800000
10426	1949.000000
10427	3922.333333
10428	1975.924278
10429	1975.924278
10430	1910.666667
10431	4295.500000
10432	1975.924278
10433	2317.000000
10434	1975.924278
10435	1927.500000
10436	2216.600000
10437	1929.076923
10438	1975.924278
10439	1810.000000
10440	2782.000000
10441	1901.333333
10442	2376.750000
10443	1972.166667
10444	2958.000000
10445	2091.500000
10446	2653.777778
10447	1176.500000
10448	1454.000000
10449	2910.800000
10450	1591.500000
10451	2125.066667
10452	1897.000000
10453	2162.111111
10454	1912.666667
10455	2167.833333
10456	1975.924278
10457	1520.666667
10458	2098.300000
10459	1715.483871
10460	1127.750000
10461	2722.374545
10462	2749.800000
10463	1773.666667
10464	1968.166667
10465	1775.636364
10466	1975.924278
10467	1975.924278
10468	1975.924278
10469	2517.250000
10470	1377.500000
10471	2167.437500
10472	1975.924278
10473	2088.900000
10474	2521.750000
10475	2144.000000
10476	2410.285714
10477	2390.000000
10478	2133.555556
10479	1975.924278
10480	1425.500000
10481	1835.245614
10482	2630.538462
10483	1975.924278
10484	1426.000000
10485	1523.000000
10486	1344.400000
10487	2201.571429
10488	2383.421053
10489	549.000000
10490	1385.000000
10491	1648.500000
10492	1349.954545
10493	2407.388889
10494	1471.285714
10495	2547.145455
10496	1762.750000
10497	1975.924278
10498	1366.050000
10499	1152.666667
10500	1975.924278
10501	1721.473684
10502	1883.733333
10503	2375.450000
10504	3377.444444
10505	1770.500000
10506	1628.500000
10507	1423.125000
10508	2229.500000
10509	1273.250000
10510	1607.285714
10511	1164.222222
10512	1705.000000
10513	1396.000000
10514	1564.888889
10515	1975.000000
10516	2510.714286
10517	1789.083333
10518	1807.428571
10519	2376.333333
10520	1684.000000
10521	1184.000000
10522	1975.924278
10523	1097.500000
10524	2156.750000
10525	1982.428571
10526	1887.333333
10527	2195.000000
10528	1715.750000
10529	1738.925926
10530	2337.100000
10531	1434.500000
10532	1934.000000
10533	1934.750000
10534	2751.750000
10535	1683.666667
10536	2566.800000
10537	1443.588235
10538	2628.500000
10539	1499.611111
10540	2227.666667
10541	1975.924278
10542	2863.789474
10543	1414.666667
10544	1975.924278
10545	1975.924278
10546	1762.666667
10547	2467.323529
10548	1815.888889
10549	1609.000000
10550	2339.750000
10551	1338.600000
10552	1429.230769
10553	2310.333333
10554	1510.000000
10555	1068.400000
10556	1066.800000
10557	1576.200000
10558	1529.500000
10559	2185.714286
10560	1975.924278
10561	2653.600000
10562	2596.995261
10563	1288.875000
10564	1951.000000
10565	1416.223022
10566	1923.862069
10567	2724.000000
10568	1975.924278
10569	1839.000000
10570	1975.924278
10571	2436.666667
10572	2182.000000
10573	2705.363636
10574	2532.884615
10575	2125.600000
10576	2230.000000
10577	1975.924278
10578	1843.000000
10579	1353.654321
10580	2157.000000
10581	1806.571429
10582	1387.857143
10583	1674.000000
10584	704.000000
10585	1482.750000
10586	2707.181818
10587	1975.924278
10588	1975.924278
10589	2220.888889
10590	708.000000
10591	1975.924278
10592	2194.000000
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10595	1975.924278
10596	1535.166667
10597	1814.333333
10598	1938.733333
10599	1152.250000
10600	1503.571429
10601	1052.666667
10602	1458.000000
10603	1498.500000
10604	2323.071429
10605	1743.800000
10606	1327.833333
10607	1975.924278
10608	1913.624549
10609	2474.700000
10610	3122.000000
10611	2321.090909
10612	2070.000000
10613	2713.619048
10614	1975.924278
10615	1318.000000
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10617	1247.400000
10618	1975.924278
10619	2235.111111
10620	1713.000000
10621	1087.200000
10622	1264.272727
10623	1975.924278
10624	1975.924278
10625	1329.000000
10626	2062.307692
10627	2083.500000
10628	1537.000000
10629	1548.000000
10630	2648.888889
10631	1444.500000
10632	1874.416667
10633	1570.818182
10634	2065.666667
10635	1885.000000
10636	1568.285714
10637	1975.924278
10638	1574.833333
10639	1778.500000
10640	2453.666667
10641	1954.333333
10642	1590.500000
10643	2349.375000
10644	2014.250000
10645	2553.823529
10646	1824.000000
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10650	1975.924278
10651	2996.000000
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10655	3084.846154
10656	2527.285714
10657	2121.250000
10658	1975.924278
10659	1551.000000
10660	2580.900000
10661	1376.833333
10662	1977.666667
10663	2155.333333
10664	2559.833333
10665	1638.057143
10666	1975.924278
10667	1385.900000
10668	1668.850000
10669	1424.250000
10670	2544.392857
10671	1983.400000
10672	1832.909091
10673	1975.924278
10674	1975.924278
10675	1479.571429
10676	2358.000000
10677	3164.333333
10678	2483.672414
10679	1817.666667
10680	1318.000000
10681	908.000000
10682	2562.571429
10683	1513.551020
10684	1466.000000
10685	1951.000000
10686	1280.250000
10687	2290.723577
10688	2010.714286
10689	2511.703297
10690	1975.924278
10691	1650.750000
10692	2362.352941
10693	1975.924278
10694	1474.333333
10695	1728.812500
10696	2663.346939
10697	2319.010000
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10699	1512.500000
10700	1631.500000
10701	2039.666667
10702	1478.500000
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10705	927.000000
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10708	1975.924278
10709	634.000000
10710	1975.924278
10711	1975.924278
10712	1677.375000
10713	2820.222222
10714	2020.000000
10715	2631.833333
10716	1975.924278
10717	1879.000000
10718	1867.571429
10719	2438.333333
10720	2072.266667
10721	1759.500000
10722	1529.250000
10723	1427.000000
10724	1975.924278
10725	1326.000000
10726	1126.000000
10727	1355.705882
10728	1975.924278
10729	1550.333333
10730	1329.200000
10731	1843.500000
10732	1478.000000
10733	1975.924278
10734	2409.357143
10735	983.500000
10736	1975.924278
10737	2158.500000
10738	2278.000000
10739	1400.000000
10740	2173.857143
10741	2014.000000
10742	4692.000000
10743	2541.642857
10744	2072.890625
10745	1434.642384
10746	1594.500000
10747	1428.400000
10748	1013.833333
10749	1700.000000
10750	2150.500000
10751	1628.000000
10752	1956.333333
10753	1975.924278
10754	2308.000000
10755	1477.666667
10756	1721.900000
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10759	2442.000000
10760	1975.924278
10761	1123.400000
10762	1975.924278
10763	2450.085714
10764	2331.000000
10765	1310.000000
10766	2577.625000
10767	1436.923077
10768	1975.924278
10769	994.500000
10770	1532.000000
10771	1677.333333
10772	1474.750000
10773	1950.714286
10774	1470.750000
10775	1975.924278
10776	1514.500000
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10778	1945.000000
10779	1235.500000
10780	2166.666667
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10782	1975.924278
10783	1943.000000
10784	1975.924278
10785	2337.300000
10786	1908.300000
10787	1802.000000
10788	1698.125000
10789	2257.538462
10790	1272.600000
10791	1056.800000
10792	2050.500000
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10795	2611.750000
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10798	1624.266667
10799	2336.000000
10800	2329.652174
10801	2175.000000
10802	1975.924278
10803	1735.000000
10804	3825.000000
10805	2294.750000
10806	1987.333333
10807	1975.924278
10808	3145.000000
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10810	2479.000000
10811	2100.200000
10812	1723.000000
10813	2693.937500
10814	2344.285714
10815	1089.142857
10816	3999.000000
10817	1975.924278
10818	2599.769231
10819	2457.909091
10820	1775.541667
10821	2126.777778
10822	1975.924278
10823	1273.125000
10824	2220.333333
10825	1431.333333
10826	1664.571429
10827	1975.924278
10828	1345.000000
10829	1975.924278
10830	1625.714286
10831	1849.250000
10832	2322.000000
10833	2550.833333
10834	1534.627907
10835	2107.555556
10836	2669.800000
10837	1975.924278
10838	2295.956522
10839	1964.285714
10840	2547.571429
10841	2558.357143
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10844	1222.000000
10845	1832.066667
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10847	2487.375000
10848	1442.333333
10849	1235.000000
10850	1898.617647
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10857	1293.421053
10858	1975.924278
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10860	1975.924278
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10862	2001.821429
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10864	1477.388889
10865	1469.500000
10866	1975.924278
10867	1306.666667
10868	2051.500000
10869	1494.000000
10870	1975.924278
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10873	1975.924278
10874	2308.071429
10875	1246.000000
10876	1618.833333
10877	1777.500000
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10879	4492.000000
10880	1763.157895
10881	3291.000000
10882	1524.200000
10883	2136.076923
10884	1942.428571
10885	2992.229730
10886	2214.405797
10887	2406.071429
10888	3906.467742
10889	1616.952381
10890	2564.555556
10891	1965.454545
10892	2737.720000
10893	1052.428571
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10895	1348.666667
10896	2176.357143
10897	1504.466667
10898	2450.125000
10899	1103.777778
10900	2321.539984
10901	1845.903226
10902	2799.757576
10903	2980.744186
10904	3772.575000
10905	1420.813084
10906	1324.928571
10907	1774.719298
10908	1330.660714
10909	2450.444444
10910	1508.000000
10911	2124.111111
10912	4125.428571
10913	3288.500000
10914	1079.000000
10915	1585.444444
10916	2059.933333
10917	1320.250000
10918	3537.444444
10919	1429.812500
10920	3585.923780
10921	3324.250000
10922	1497.750000
10923	2978.310924
10924	2473.687500
10925	1616.875000
10926	2464.047619
10927	2425.286364
10928	1911.722222
10929	1759.000000
10930	1658.743590
10931	2177.928571
10932	1975.924278
10933	2011.233333
10934	1253.937500
10935	2281.400000
10936	2647.947368
10937	3527.195122
10938	3989.211429
10939	1813.333333
10940	2843.920000
10941	2344.711864
10942	1796.500000
10943	2386.714286
10944	1713.333333
10945	2068.285714
10946	1938.562500
10947	1237.250000
10948	1881.294118
10949	2483.346667
10950	1853.250000
10951	1392.400000
10952	2673.196721
10953	1979.642857
10954	1456.250000
10955	2483.870504
10956	2357.361702
10957	2344.161290
10958	1686.100000
10959	2153.866667
10960	2174.391304
10961	1379.000000
10962	3483.500000
10963	4125.846154
10964	2050.750000
10965	1887.750000
10966	1424.000000
10967	1829.500000
10968	2439.914286
10969	2340.222222
10970	1632.818182
10971	2269.500000
10972	1495.333333
10973	2650.864865
10974	2179.769231
10975	1774.333333
10976	2892.461538
10977	1394.789474
10978	2196.720000
10979	2532.750000
10980	1809.935484
10981	2060.411765
10982	1634.900000
10983	2317.000000
10984	3283.120000
10985	1202.375000
10986	1452.400000
10987	2648.200000
10988	1713.194444
10989	1258.250000
10990	693.000000
10991	1975.924278
10992	2048.848485
10993	2007.615385
10994	1876.666667
10995	1398.357143
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10997	2073.362832
10998	1406.300000
10999	1975.924278
11000	1975.924278
11001	1829.500000
11002	1975.924278
11003	3058.162791
11004	1151.000000
11005	2593.222222
11006	4042.684211
11007	2416.769231
11008	3476.493333
11009	1932.454545
11010	2756.050000
11011	1853.714286
11012	2308.081081
11013	910.000000
11014	1505.000000
11015	2586.700000
11016	1933.666667
11017	2013.857143
11018	2226.558140
11019	2415.777778
11020	1322.461538
11021	1975.924278
11022	3321.300000
11023	1724.151007
11024	1225.666667
11025	4151.107143
11026	3120.375000
11027	2268.500000
11028	2815.287356
11029	1188.666667
11030	2212.134146
11031	3047.392857
11032	1482.303030
11033	1750.121212
11034	2640.339623
11035	1690.833333
11036	2148.055556
11037	2560.952381
11038	1885.121212
11039	2454.529412
11040	2378.450000
11041	2311.360000
11042	1973.333333
11043	2575.734375
11044	1242.153846
11045	2399.500000
11046	2778.452381
11047	2735.686747
11048	1722.525000
11049	2145.733333
11050	2194.863636
11051	1576.076923
11052	1752.666667
11053	1747.581395
11054	2470.559322
11055	1890.409091
11056	1521.500000
11057	2626.359375
11058	2548.813433
11059	3179.731343
11060	1578.743590
11061	2237.586207
11062	1289.900000
11063	2672.896226
11064	1697.464286
11065	2575.130952
11066	1996.411765
11067	1848.660714
11068	2676.476190
11069	1057.192308
11070	1975.924278
11071	1687.181818
11072	1157.300000
11073	1689.760000
11074	1769.711111
11075	2024.500000
11076	2188.333333
11077	1600.884615
11078	1136.230769
11079	2463.556604
11080	1543.476190
11081	2756.625000
11082	2426.297297
11083	2223.057143
11084	2922.620690
11085	2080.833333
11086	1975.924278
11087	2490.835714
11088	2813.000000
11089	1768.125000
11090	1941.285714
11091	2136.166667
11092	2057.411765
11093	1150.920000
11094	2701.375000
11095	1577.600000
11096	1986.944444
11097	2324.599507
11098	2304.891026
11099	2071.388889
11100	2923.531746
11101	2475.035088
11102	1904.071429
11103	2417.272727
11104	3032.714286
11105	1994.125000
11106	1936.788235
11107	1975.924278
11108	1680.142857
11109	2290.727273
11110	1502.750000
11111	2583.028571
11112	1814.250000
11113	1230.000000
11114	1056.066667
11115	2007.888889
11116	2535.210526
11117	1750.486842
11118	2056.875000
11119	2790.680000
11120	2628.083333
11121	2255.336842
11122	2625.494253
11123	1279.250000
11124	1390.153846
11125	1949.357143
11126	1498.625000
11127	1882.857143
11128	1716.309524
11129	1050.090909
11130	2311.000000
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11133	1660.777778
11134	2831.200000
11135	2885.500000
11136	1381.794872
11137	1255.095238
11138	2557.941176
11139	1531.400000
11140	1166.333333
11141	1709.651163
11142	3039.809524
11143	2359.666667
11144	2797.833333
11145	2685.186047
11146	1031.714286
11147	1975.924278
11148	1997.642857
11149	1983.500000
11150	1476.500000
11151	1618.500000
11152	1453.153846
11153	2066.500000
11154	3375.631068
11155	1518.875000
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11157	2180.666667
11158	2105.700000
11159	1959.800000
11160	1056.000000
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11163	1429.470588
11164	5213.555556
11165	4027.391188
11166	4448.944444
11167	6334.772727
11168	2468.500000
11169	1975.924278
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11172	1975.924278
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11174	1975.924278
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11182	1975.924278
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11186	2500.000000
11187	1975.924278
11188	1975.924278
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11190	1975.924278
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11200	1292.000000
11201	8272.461538
11202	1975.924278
11203	1684.000000
11204	4345.600000
11205	5945.750000
11206	1124.333333
11207	3679.777778
11208	1550.000000
11209	1975.924278
11210	4570.000000
11211	1975.924278
11212	3125.000000
11213	1975.924278
11214	900.000000
11215	6390.884615
11216	2000.000000
11217	1975.924278
11218	1165.000000
11219	6593.064516
11220	685.000000
11221	1575.333333
11222	1162.500000
11223	840.000000
11224	1960.000000
11225	1975.924278
11226	1059.000000
11227	5997.600000
11228	2845.000000
11229	2247.000000
11230	2677.200000
11231	4433.117647
11232	1821.500000
11233	2731.000000
11234	1975.924278
11235	2277.000000
11236	1975.924278
11237	974.000000
11238	571.000000
11239	4123.625000
11240	3403.500000
11241	3259.750000
11242	6169.400000
11243	6676.391304
11244	2204.250000
11245	1975.924278
11246	6275.193750
11247	4337.938776
11248	1070.000000
11249	1933.750000
11250	2859.000000
11251	2449.000000
11252	1975.924278
11253	1975.924278
11254	400.000000
11255	5140.388889
11256	3487.500000
11257	3915.176471
11258	3322.050000
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11261	2067.000000
11262	1299.666667
11263	3385.500000
11264	1523.500000
11265	4961.409091
11266	4649.000000
11267	5164.214286
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11270	1604.666667
11271	568.000000
11272	2805.666667
11273	3211.800000
11274	4000.000000
11275	1810.166667
11276	2778.000000
11277	1975.924278
11278	925.500000
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11280	2809.750000
11281	4879.666667
11282	1796.500000
11283	1975.924278
11284	2734.000000
11285	2917.000000
11286	6873.276596
11287	1975.924278
11288	1975.924278
11289	2992.000000
11290	1975.924278
11291	1975.924278
11292	2445.266667
11293	6055.444444
11294	1975.924278
11295	1975.924278
11296	2250.000000
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11298	1975.924278
11299	1975.924278
11300	3796.500000
11301	3346.000000
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11309	1975.924278
11310	1250.000000
11311	818.666667
11312	3513.735294
11313	4785.212121
11314	1975.924278
11315	1975.924278
11316	1975.924278
11317	1348.000000
11318	1975.924278
11319	2902.666667
11320	2195.166667
11321	1975.924278
11322	1975.924278
11323	1975.924278
11324	1975.924278
11325	1975.924278
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11330	2078.250000
11331	1975.924278
11332	2053.666667
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11335	1975.924278
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11340	2295.500000
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11342	1975.924278
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11347	1975.924278
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11352	1975.924278
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11355	1975.924278
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11360	1778.333333
11361	1975.924278
11362	1975.924278
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11364	2779.333333
11365	3082.250000
11366	1975.924278
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12212	1969.857143
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12214	1975.924278
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12218	1975.924278
12219	445.000000
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12225	1975.924278
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12230	1975.924278
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12253	1975.924278
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12263	1975.924278
12264	1975.924278
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12271	1975.924278
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12280	3437.388889
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12325	2113.085714
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12401	1975.924278
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12420	1975.924278
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12430	1975.924278
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12433	1975.924278
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12488	1975.924278
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12512	1975.924278
12513	1975.924278
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12521	1975.924278
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12529	1975.924278
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12531	1975.924278
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12533	1975.924278
12534	1975.924278
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12536	1975.924278
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12550	1975.924278
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12562	1975.924278
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12580	1975.924278
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12617	1975.924278
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12625	1975.924278
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12629	1975.924278
12630	1151.500000
12631	1975.924278
12632	833.000000
12633	1975.924278
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12639	1975.924278
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12665	1975.924278
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12667	1710.000000
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12688	1975.924278
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12705	1975.924278
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12711	1975.924278
12712	1191.062500
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12724	1975.924278
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12746	1975.924278
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12750	1975.924278
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12752	1975.924278
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12760	2004.444444
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12775	1975.924278
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12784	1975.924278
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12801	1975.924278
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12812	1975.924278
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12816	1975.924278
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12822	1975.924278
12823	1975.924278
12824	1708.500000
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12830	1975.924278
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12833	1975.924278
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12836	1975.924278
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12838	1975.924278
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12845	1975.924278
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12850	879.500000
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12875	1975.924278
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12881	1975.924278
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12895	1384.666667
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12912	1975.924278
12913	1975.924278
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12920	1975.924278
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12925	1975.924278
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12976	1978.214286
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12980	1975.924278
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13005	1975.924278
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13017	1975.924278
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13029	1975.924278
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13033	1975.924278
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13037	1975.924278
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13045	2435.724138
13046	1975.924278
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13054	1975.924278
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13056	1975.924278
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13058	1975.924278
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13060	1975.924278
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13063	2011.236220
13064	1975.924278
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13094	1975.924278
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13211	1656.303030
13212	2667.600000
13213	1709.500000
13214	1975.924278
13215	3117.588235
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13228	1975.924278
13229	2002.466667
13230	1333.000000
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13239	4079.542484
13240	1737.444444
13241	1963.857143
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13249	2035.166667
13250	1921.600000
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13253	1953.117647
13254	3997.857143
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13259	1975.924278
13260	2755.846154
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13264	1556.600000
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13280	1850.000000
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13290	1975.924278
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13296	1975.924278
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13299	1569.500000
13300	2978.514286
13301	1737.916667
13302	1102.714286
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13305	1975.924278
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13310	1609.000000
13311	2047.875000
13312	2094.180328
13313	4583.444444
13314	1712.315789
13315	1864.500000
13316	3221.500000
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13318	2209.000000
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13320	1975.924278
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13325	2086.000000
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13330	1966.642857
13331	2634.647059
13332	1855.000000
13333	2521.733333
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13335	3224.950000
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13337	1975.924278
13338	1975.924278
13339	1968.714286
13340	4047.600000
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13349	1975.924278
13350	3102.876923
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13360	663.000000
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13364	1831.093023
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13367	1684.714286
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13380	1784.500000
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13399	2090.869565
13400	2364.333333
13401	2350.600000
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13410	1975.924278
13411	2883.821429
13412	3702.971831
13413	1721.800000
13414	1660.285714
13415	3702.333333
13416	1590.400000
13417	3633.000000
13418	2266.833333
13419	2429.636364
13420	2045.375000
13421	1977.400000
13422	2087.800000
13423	2000.000000
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13427	1975.924278
13428	4245.883333
13429	1914.000000
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13431	2663.411765
13432	2494.583333
13433	3957.029126
13434	1474.750000
13435	2114.076923
13436	4448.131313
13437	2540.222222
13438	2079.333333
13439	2155.500000
13440	1975.924278
13441	4434.008850
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13445	2613.500000
13446	1849.090909
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13450	2776.333333
13451	1915.200000
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13453	2291.363636
13454	3735.200000
13455	2510.578947
13456	1814.800000
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13458	2604.250000
13459	1978.285714
13460	1367.666667
13461	2159.928571
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13463	1564.000000
13464	3674.438710
13465	1546.000000
13466	2336.363636
13467	2244.200000
13468	3253.962963
13469	3548.809524
13470	2495.583333
13471	2287.000000
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13473	1362.333333
13474	1975.924278
13475	1811.333333
13476	1838.750000
13477	1886.333333
13478	2560.259259
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13480	1757.000000
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13482	2317.884615
13483	4459.923077
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13485	2526.333333
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13487	2429.800000
13488	1350.625000
13489	2006.750000
13490	1715.142857
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13492	4275.424354
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13494	2443.439024
13495	1273.666667
13496	4000.055556
13497	1524.031496
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13499	2837.000000
13500	2335.746479
13501	1992.416923
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13505	2741.842105
13506	2157.418605
13507	1570.214286
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13510	2670.846154
13511	3027.666667
13512	3735.000000
13513	1412.400000
13514	1555.500000
13515	3608.750000
13516	2421.600000
13517	1975.924278
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13520	2733.500000
13521	2044.013699
13522	1975.924278
13523	502.000000
13524	1596.500000
13525	2107.687500
13526	4291.181818
13527	4519.313433
13528	1195.250000
13529	2600.833333
13530	1775.142857
13531	1816.600000
13532	2138.816327
13533	1138.375000
13534	1912.000000
13535	6766.000000
13536	3381.923077
13537	2006.857143
13538	1959.520548
13539	1993.846154
13540	1300.250000
13541	1975.924278
13542	2324.409091
13543	1932.365854
13544	1608.545455
13545	2943.058824
13546	4134.476190
13547	3627.769231
13548	2587.469231
13549	1745.090909
13550	2749.147059
13551	4348.111111
13552	1975.924278
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13560	1975.924278
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13564	3538.024691
13565	2665.714286
13566	1854.700000
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13568	1975.924278
13569	1975.924278
13570	2619.880952
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13577	3810.339806
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13579	3192.000000
13580	1873.125000
13581	1975.924278
13582	3244.534884
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13584	1191.800000
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13586	1016.650000
13587	1975.924278
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13589	4111.636364
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13594	1975.924278
13595	2197.028986
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13599	2554.600000
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13601	2269.666667
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13604	1959.591837
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13609	2678.735294
13610	1671.333333
13611	1722.728814
13612	3790.441176
13613	1466.363636
13614	2779.693299
13615	3256.171429
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13619	4029.162338
13620	3009.697674
13621	2289.666667
13622	4237.833333
13623	4993.398329
13624	2471.093750
13625	1975.924278
13626	3673.942408
13627	1975.924278
13628	4027.666667
13629	2389.500000
13630	2463.115385
13631	2285.095890
13632	3335.897436
13633	3508.666667
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13635	2361.166667
13636	2546.500000
13637	1471.000000
13638	5589.380952
13639	1672.900000
13640	1975.924278
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13649	1944.900000
13650	2356.800000
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13652	1962.553846
13653	1697.363636
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13660	2022.500000
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13664	2198.500000
13665	1099.666667
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13667	4754.421053
13668	2451.281250
13669	1826.750000
13670	1499.000000
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13674	1975.924278
13675	2474.000000
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13680	1451.692308
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13685	2218.400000
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13699	1568.600000
13700	1975.924278
13701	2076.352941
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13705	5378.500000
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13710	2737.900000
13711	3204.750000
13712	2389.411765
13713	3903.116279
13714	1609.071429
13715	3241.391304
13716	4471.454545
13717	2600.200000
13718	3917.347826
13719	1442.666667
13720	1675.800000
13721	1043.250000
13722	1975.924278
13723	3090.333333
13724	4345.902655
13725	3662.000000
13726	2058.500000
13727	2104.275862
13728	3021.814815
13729	821.000000
13730	2958.333333
13731	1427.750000
13732	3239.800000
13733	1975.924278
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13735	4653.833333
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13737	2327.000000
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13739	4190.817460
13740	1639.250000
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13750	3288.777778
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13760	2477.222222
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13764	3266.444444
13765	3023.666667
13766	2790.936508
13767	3295.534722
13768	2411.608696
13769	3893.564149
13770	1689.357143
13771	1975.924278
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13775	4129.913043
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13780	1878.523810
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13784	1290.500000
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13788	3503.833333
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13791	1975.924278
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13801	6046.515152
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13803	1994.142857
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13810	3783.666667
13811	3435.750000
13812	5565.489333
13813	3095.277778
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13820	1776.800000
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13822	2090.812500
13823	1975.924278
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13825	2152.244444
13826	2056.676471
13827	2040.263158
13828	2303.976190
13829	2244.800000
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13835	3387.627737
13836	1620.555556
13837	1515.000000
13838	1865.173913
13839	1740.846154
13840	1933.774194
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13844	2253.900000
13845	1584.666667
13846	1997.857143
13847	2931.775862
13848	1513.800000
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13850	2555.153846
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13857	1975.924278
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13860	4096.525773
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13864	3256.204082
13865	1894.222222
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13868	3102.983871
13869	2993.333333
13870	1676.875000
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13875	3130.816514
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13877	2621.055556
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13880	1415.000000
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14709	1975.924278
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14712	1975.924278
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14715	1975.924278
14716	2380.600000
14717	2616.444444
14718	4154.000000
14719	1800.000000
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14721	2017.253968
14722	2101.133333
14723	3263.000000
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14725	2707.400000
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14727	3284.500000
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14729	2407.571429
14730	3061.230769
14731	2514.333333
14732	5388.181818
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14735	2892.200000
14736	1929.500000
14737	2137.666667
14738	2090.800000
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14740	2785.564607
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14743	1916.500000
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14747	1975.924278
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14750	3267.600000
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14753	1975.924278
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14759	1941.800000
14760	3654.750000
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14766	2086.200000
14767	2840.764706
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14770	2499.090909
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14785	1975.924278
14786	1975.924278
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14799	3126.500000
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14801	1910.250000
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14808	1975.924278
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14821	3830.581818
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14823	4088.227273
14824	1975.924278
14825	3267.714286
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14827	2569.862069
14828	2894.894737
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14845	2400.428571
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14850	2486.000000
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14860	4082.461538
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14865	3752.583333
14866	2978.636364
14867	6792.305882
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14870	2253.107143
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14875	3599.470588
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14877	4039.250000
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14890	1975.924278
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14893	1975.924278
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14895	2612.777778
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14899	2917.285714
14900	3850.180952
14901	3539.333333
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14904	1975.924278
14905	2712.416667
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14910	1975.924278
14911	1442.250000
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14914	2758.000000
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14922	1826.857143
14923	1975.924278
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14925	1523.500000
14926	1975.924278
14927	2329.000000
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14930	1975.924278
14931	3370.666667
14932	2446.200000
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14934	1614.571429
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14937	1747.304348
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14941	3059.500000
14942	1975.924278
14943	1975.924278
14944	2833.923077
14945	3007.500000
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14951	1975.924278
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14960	2802.809524
14961	1975.924278
14962	3198.500000
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14970	1975.924278
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14973	1975.924278
14974	1975.924278
14975	2149.000000
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14977	3320.200000
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14979	1967.647059
14980	1975.924278
14981	1975.924278
14982	2862.250000
14983	2090.686275
14984	2691.193548
14985	2000.553191
14986	2518.403846
14987	1460.700000
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14989	2620.000000
14990	1975.924278
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15000	1924.000000
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15020	2838.400000
15021	1666.500000
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15023	2728.600000
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15025	2207.600000
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15027	2250.250000
15028	1583.000000
15029	2888.750000
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15033	1850.000000
15034	1975.924278
15035	2725.100000
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15037	2766.250000
15038	2254.833333
15039	2794.428571
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15044	2864.777778
15045	1389.666667
15046	1700.333333
15047	2894.277778
15048	2919.376543
15049	3453.695652
15050	2356.076923
15051	1799.904762
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15053	4818.000000
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15060	3960.538462
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15064	2586.627451
15065	3402.258065
15066	2607.500000
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15068	2135.000000
15069	1975.924278
15070	2571.090909
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15077	2511.400000
15078	1975.924278
15079	2669.636364
15080	2155.175000
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15083	1975.924278
15084	1950.000000
15085	2602.779412
15086	4232.720930
15087	2943.823529
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15092	1975.924278
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15095	4417.636364
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15099	1975.924278
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15123	2007.750000
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15211	4159.933333
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15213	2182.421053
15214	2100.222222
15215	2017.000000
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15217	2566.866379
15218	2133.500000
15219	3114.207317
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15224	1975.924278
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15230	707.000000
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15232	1800.651163
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15250	1975.924278
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15254	1975.924278
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15263	1975.924278
15264	2129.666667
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15269	1975.924278
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15271	1975.924278
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15280	2303.666667
15281	1975.924278
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15283	1975.924278
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15285	1975.924278
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15292	1975.924278
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15299	2652.500000
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15301	1261.000000
15302	1975.924278
15303	1975.924278
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15310	1765.365385
15311	1442.000000
15312	1321.500000
15313	2082.600000
15314	1577.500000
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15316	1563.615385
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15319	1254.500000
15320	1975.924278
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15322	1975.924278
15323	1975.924278
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15325	1975.924278
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15327	1975.924278
15328	1975.924278
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15330	1975.924278
15331	1975.924278
15332	603.000000
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15335	1975.924278
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15338	1975.924278
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15340	1704.750000
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15343	1975.924278
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15346	1975.924278
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15350	1736.000000
15351	1975.924278
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15354	1975.924278
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15358	1975.924278
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15360	1826.615385
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15364	1975.924278
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15366	1975.924278
15367	1975.924278
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15380	1800.444444
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15384	1975.924278
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15394	1975.924278
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15399	1975.924278
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15401	1561.818182
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15410	1449.000000
15411	1975.924278
15412	1304.000000
15413	2005.750000
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15419	730.500000
15420	1975.924278
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15422	1975.924278
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15428	1975.924278
15429	1975.924278
15430	1975.924278
15431	1975.924278
15432	1975.924278
15433	1975.924278
15434	1839.833333
15435	2598.666667
15436	1975.924278
15437	2500.000000
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15440	1975.924278
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15445	1975.924278
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15453	1975.924278
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15455	1975.924278
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15463	1975.924278
15464	1975.924278
15465	1802.200000
15466	1205.000000
15467	1975.924278
15468	781.000000
15469	1017.250000
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15471	1975.924278
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15475	845.000000
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15477	3769.600000
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15479	1975.924278
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15485	2494.500000
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15488	1410.470588
15489	1975.924278
15490	2277.250000
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15501	955.500000
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15510	2229.000000
15511	1975.924278
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15513	1975.924278
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15515	1975.924278
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15521	1975.924278
15522	1975.924278
15523	1709.500000
15524	1975.924278
15525	1725.888889
15526	1975.924278
15527	1742.222222
15528	1957.666667
15529	1975.924278
15530	945.000000
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15532	1975.924278
15533	1646.705882
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15538	1997.500000
15539	1132.800000
15540	1975.924278
15541	1975.924278
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15543	1975.924278
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15545	1926.500000
15546	1132.000000
15547	1975.924278
15548	1281.500000
15549	1748.250000
15550	1271.333333
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18064	1637.400000
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18066	1934.013158
18067	2084.500000
18068	1767.500000
18069	1436.000000
18070	2116.333333
18071	2643.250000
18072	1594.000000
18073	2012.000000
18074	1860.122807
18075	1503.866667
18076	1350.500000
18077	2046.428571
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18079	1488.000000
18080	1656.470588
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18084	1579.000000
18085	1450.000000
18086	1096.166667
18087	2395.666667
18088	1233.500000
18089	935.500000
18090	2240.095238
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18092	1975.924278
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18095	1155.333333
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18099	2294.000000
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18111	1975.924278
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18119	1975.924278
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18123	1975.924278
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18134	1975.924278
18135	1600.600000
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18147	1975.924278
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18164	1975.924278
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18194	1975.924278
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18200	1975.924278
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18208	1975.924278
18209	1975.924278
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18213	583.500000
18214	1769.000000
18215	1612.000000
18216	1975.924278
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18218	1975.924278
18219	1701.708333
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18221	1975.924278
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18224	1975.924278
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18228	1975.924278
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18233	1975.924278
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18240	1975.924278
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18243	1975.924278
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18245	1975.924278
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18259	1975.924278
18260	1975.924278
18261	1975.924278
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18265	1975.924278
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18267	1975.924278
18268	1975.924278
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18274	1975.924278
18275	1459.919355
18276	1975.924278
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18283	1975.924278
18284	1975.924278
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18291	1975.924278
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18294	1975.924278
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18298	1975.924278
18299	1510.000000
18300	1975.924278
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18303	1975.924278
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18311	1975.924278
18312	1975.924278
18313	1271.000000
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18315	2478.500000
18316	1241.500000
18317	1141.750000
18318	1437.194444
18319	1734.000000
18320	1975.924278
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18322	1975.924278
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18325	2034.444444
18326	1975.924278
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18330	667.000000
18331	1975.924278
18332	2642.888889
18333	1367.727273
18334	1975.924278
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18340	1975.924278
18341	1000.000000
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18348	1975.924278
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18353	1975.924278
18354	1981.307692
18355	1975.924278
18356	1975.924278
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18359	1975.924278
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18362	1975.924278
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18364	767.000000
18365	1975.924278
18366	1965.000000
18367	1975.924278
18368	1975.924278
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18371	1975.924278
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18373	2167.833333
18374	1975.924278
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18377	1975.924278
18378	1975.924278
18379	1975.924278
18380	3412.755500
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18386	1975.924278
18387	1975.924278
18388	3079.321053
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18391	1975.924278
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18396	1975.924278
18397	1975.924278
18398	1972.428571
18399	1763.916667
18400	1924.000000
18401	2482.666667
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18403	1975.924278
18404	1975.924278
18405	1975.924278
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18409	1975.924278
18410	1683.000000
18411	1975.924278
18412	1975.924278
18413	1742.736842
18414	1975.924278
18415	1975.924278
18416	1975.924278
18417	1561.600000
18418	1975.924278
18419	1975.924278
18420	2726.476190
18421	1975.924278
18422	2184.598214
18423	1975.924278
18424	1975.924278
18425	1396.000000
18426	1506.125000
18427	2797.611111
18428	1488.000000
18429	875.500000
18430	1942.285714
18431	1975.924278
18432	1939.166667
18433	1694.545455
18434	1975.924278
18435	2554.771429
18436	1919.555556
18437	1975.924278
18438	1975.924278
18439	1504.200000
18440	1971.764706
18441	2249.314815
18442	1527.750000
18443	1975.924278
18444	1975.924278
18445	1975.924278
18446	1975.924278
18447	1975.924278
18448	1959.272727
18449	1975.924278
18450	1975.924278
18451	1975.924278
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18453	1975.924278
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18455	1975.924278
18456	1975.924278
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18459	1975.924278
18460	1975.924278
18461	2172.062500
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18463	1975.924278
18464	1740.437500
18465	1975.924278
18466	1975.924278
18467	1975.924278
18468	1975.924278
18469	1975.924278
18470	1975.924278
18471	2246.923851
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18473	1975.924278
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18475	1975.924278
18476	1975.924278
18477	1975.924278
18478	1975.924278
18479	1982.700000
18480	1975.924278
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18483	1975.924278
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18485	1488.200000
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18487	1975.924278
18488	1975.924278
18489	1975.924278
18490	1975.924278
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18495	1712.427184
18496	1975.924278
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18498	1975.924278
18499	2646.000000
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18501	1492.000000
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18503	1975.924278
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18506	1975.924278
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18510	1430.705882
18511	1975.924278
18512	1975.924278
18513	1975.924278
18514	1975.924278
18515	1975.924278
18516	1975.924278
18517	1975.924278
18518	1975.924278
18519	1975.924278
18520	1975.924278
18521	1975.924278
18522	1975.924278
18523	2569.916667
18524	1975.924278
18525	1975.924278
18526	1975.924278
18527	1975.924278
18528	2252.363636
18529	1922.444444
18530	1975.924278
18531	2080.592593
18532	1975.924278
18533	2600.280000
18534	1975.924278
18535	1873.750000
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18537	1975.924278
18538	1975.924278
18539	1555.666667
18540	1975.924278
18541	2411.759259
18542	1975.924278
18543	1410.166667
18544	1975.924278
18545	1701.431034
18546	1975.924278
18547	1791.200000
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18549	1975.924278
18550	1975.924278
18551	1975.924278
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18554	1975.924278
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18560	2247.769231
18561	1975.924278
18562	1975.924278
18563	1975.924278
18564	2460.546875
18565	2009.000000
18566	1975.924278
18567	1975.924278
18568	1430.636364
18569	1975.924278
18570	1975.924278
18571	1975.924278
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18574	1975.924278
18575	2957.461538
18576	1385.977778
18577	1938.333333
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18579	1664.108108
18580	1382.600000
18581	1682.588235
18582	1315.500000
18583	2688.421053
18584	2642.342857
18585	1898.600000
18586	1975.924278
18587	1975.924278
18588	1975.924278
18589	1178.978723
18590	1975.924278
18591	1975.924278
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18593	1975.924278
18594	1975.924278
18595	1975.924278
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18599	1975.924278
18600	1975.924278
18601	1560.272727
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18603	1975.924278
18604	1975.924278
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18609	1975.924278
18610	1975.924278
18611	1975.924278
18612	1975.924278
18613	1503.777778
18614	1975.924278
18615	1975.924278
18616	1569.666667
18617	1975.924278
18618	2213.379310
18619	1839.444444
18620	2015.510417
18621	2008.000000
18622	1975.924278
18623	1975.924278
18624	1975.924278
18625	2296.600000
18626	2859.600000
18627	1975.924278
18628	2196.714286
18629	1975.924278
18630	2388.650000
18631	2014.307692
18632	1975.924278
18633	1975.924278
18634	1975.924278
18635	1975.924278
18636	3054.142857
18637	1975.924278
18638	1975.924278
18639	1975.924278
18640	1975.924278
18641	2729.289474
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18643	1975.924278
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18645	1975.924278
18646	1975.924278
18647	935.000000
18648	1989.682927
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18650	1975.924278
18651	1975.924278
18652	1975.924278
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18655	1975.924278
18656	1968.348548
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18658	1975.924278
18659	1975.924278
18660	2492.629630
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18663	1975.924278
18664	1193.200000
18665	1651.000000
18666	1975.924278
18667	1975.924278
18668	1975.924278
18669	1975.924278
18670	1975.924278
18671	1975.924278
18672	1975.924278
18673	1975.924278
18674	1975.924278
18675	1889.833333
18676	1975.924278
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18678	1975.924278
18679	1975.924278
18680	2752.250000
18681	1975.924278
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18683	2539.375000
18684	2021.625000
18685	1518.363208
18686	2111.611111
18687	1868.653846
18688	1928.666667
18689	1975.924278
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18691	1975.924278
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18695	1975.924278
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18699	1975.924278
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18701	1975.924278
18702	1975.924278
18703	1975.924278
18704	1975.924278
18705	1685.400000
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18707	1975.924278
18708	1993.030303
18709	1975.924278
18710	2007.500000
18711	1821.300000
18712	1975.924278
18713	1780.428571
18714	1876.300000
18715	1975.924278
18716	1442.200000
18717	1975.924278
18718	1975.924278
18719	1811.465116
18720	1548.923077
18721	1535.142857
18722	1666.666667
18723	1631.000000
18724	1388.126316
18725	1697.222222
18726	1975.924278
18727	1975.924278
18728	2323.250000
18729	1786.074074
18730	1774.526316
18731	1975.924278
18732	1886.672131
18733	4457.967742
18734	1468.625000
18735	1975.924278
18736	1067.000000
18737	1975.924278
18738	3245.000000
18739	1975.924278
18740	1975.924278
18741	1975.924278
18742	1982.000000
18743	1975.924278
18744	2156.428571
18745	1563.500000
18746	1975.924278
18747	2183.166667
18748	1975.924278
18749	1389.000000
18750	2034.000000
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18754	2174.000000
18755	1885.200000
18756	1954.500000
18757	980.066667
18758	2752.214286
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18760	1762.909091
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18763	1975.924278
18764	629.000000
18765	1975.924278
18766	1569.714286
18767	1366.500000
18768	1502.333333
18769	2538.811111
18770	2577.500000
18771	1975.924278
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18774	1689.666667
18775	997.400000
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18777	1502.333333
18778	1975.924278
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18780	1133.000000
18781	1975.924278
18782	1975.924278
18783	1975.924278
18784	3035.476190
18785	1968.000000
18786	1975.924278
18787	1975.924278
18788	1207.500000
18789	1975.924278
18790	1514.800000
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18794	1975.924278
18795	2619.000000
18796	1975.924278
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18798	1975.924278
18799	2950.982759
18800	2803.571429
18801	2514.571429
18802	1994.500000
18803	1975.924278
18804	3528.222222
18805	1867.867647
18806	2287.666667
18807	2123.000000
18808	2426.533333
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18810	2009.000000
18811	1414.600000
18812	1975.924278
18813	912.857143
18814	1499.000000
18815	1179.166667
18816	1632.875000
18817	1622.666667
18818	1557.666667
18819	1975.924278
18820	1323.000000
18821	1819.679612
18822	1975.924278
18823	1687.500000
18824	3354.105263
18825	1975.924278
18826	1615.000000
18827	2333.000000
18828	1975.924278
18829	4341.333333
18830	1769.750000
18831	1492.583333
18832	1291.818182
18833	1663.400000
18834	1975.924278
18835	2846.250000
18836	1975.924278
18837	2660.000000
18838	1834.500000
18839	1975.924278
18840	2441.375000
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18842	1975.924278
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18844	1975.924278
18845	1167.000000
18846	1975.924278
18847	1975.924278
18848	2381.663300
18849	1505.444444
18850	1975.924278
18851	1975.924278
18852	1975.924278
18853	1975.924278
18854	1976.000000
18855	1975.924278
18856	1408.142857
18857	1502.375000
18858	1551.075000
18859	1975.924278
18860	2195.650000
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18862	1637.846154
18863	2124.444444
18864	1974.571429
18865	1930.282051
18866	1393.400000
18867	1965.500000
18868	1133.500000
18869	1668.318182
18870	945.000000
18871	1975.924278
18872	1975.924278
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18874	2770.000000
18875	1309.500000
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18877	1975.924278
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18879	2331.375000
18880	1975.924278
18881	1507.000000
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18883	1387.500000
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18885	1872.043478
18886	1975.924278
18887	1975.924278
18888	1975.924278
18889	1765.500000
18890	1975.924278
18891	2845.000000
18892	1502.500000
18893	1990.000000
18894	1897.500000
18895	2766.052632
18896	1975.924278
18897	1975.924278
18898	1308.000000
18899	1409.666667
18900	2294.500000
18901	2404.230769
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18904	1975.924278
18905	1853.846154
18906	1975.924278
18907	1778.000000
18908	1975.924278
18909	1195.000000
18910	1722.166667
18911	1566.500000
18912	1665.750000
18913	2580.615385
18914	1538.440000
18915	1846.500000
18916	2659.571429
18917	1054.846154
18918	1975.924278
18919	1975.924278
18920	1975.924278
18921	1665.600000
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21258	1975.924278
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21353	1975.924278
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21475	1975.924278
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21510	1975.924278
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21523	1975.924278
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21531	650.833333
21532	1187.500000
21533	625.000000
21534	1370.357143
21535	1101.000000
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21537	672.000000
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21544	1033.666667
21545	662.000000
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21548	1527.769231
21549	1484.000000
21550	1266.333333
21551	1288.800000
21552	417.000000
21553	1975.924278
21554	742.666667
21555	1975.924278
21556	927.500000
21557	1975.924278
21558	751.000000
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21560	875.400000
21561	1975.924278
21562	1261.000000
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21565	1975.924278
21566	1045.000000
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21568	1975.924278
21569	1792.000000
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21572	1372.666667
21573	1975.924278
21574	537.000000
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21576	1180.333333
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21578	1975.924278
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21581	1975.924278
21582	1247.666667
21583	1062.333333
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21585	1975.924278
21586	2739.000000
21587	1975.924278
21588	1057.000000
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21594	1081.000000
21595	1975.924278
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21597	1975.924278
21598	555.000000
21599	1975.924278
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21602	1119.793103
21603	1975.924278
21604	1975.924278
21605	612.000000
21606	1975.924278
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21608	1975.924278
21609	993.600000
21610	1975.924278
21611	1454.500000
21612	883.375000
21613	734.000000
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21620	1975.924278
21621	844.000000
21622	1975.924278
21623	834.000000
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21626	871.666667
21627	1262.000000
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21629	789.000000
21630	1975.924278
21631	2061.833333
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21633	1045.935484
21634	1975.924278
21635	1975.924278
21636	977.333333
21637	1007.500000
21638	1975.924278
21639	932.500000
21640	1378.000000
21641	1975.924278
21642	1078.666667
21643	1975.924278
21644	676.400000
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21662	1164.333333
21663	910.000000
21664	851.666667
21665	1467.500000
21666	1166.947368
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21668	1564.375000
21669	1620.350000
21670	1975.924278
21671	1266.000000
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21673	1184.166667
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21677	1143.400000
21678	1975.924278
21679	1093.600000
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21681	1975.924278
21682	894.750000
21683	1351.333333
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21687	1975.924278
21688	485.000000
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21690	1975.924278
21691	536.000000
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21694	890.923077
21695	1975.924278
21696	1975.924278
21697	612.000000
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21699	1975.924278
21700	717.250000
21701	1089.250000
21702	741.333333
21703	1540.209302
21704	1692.250000
21705	833.000000
21706	998.666667
21707	1257.680000
21708	1975.924278
21709	1349.133333
21710	1727.000000
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21712	1975.924278
21713	891.000000
21714	1975.924278
21715	1975.924278
21716	1975.924278
21717	668.000000
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21719	1408.500000
21720	1303.000000
21721	1975.924278
21722	1296.800000
21723	1462.666667
21724	1791.000000
21725	436.000000
21726	1975.924278
21727	1429.750000
21728	1975.924278
21729	1975.924278
21730	856.000000
21731	1975.924278
21732	1975.924278
21733	1827.500000
21734	1230.000000
21735	867.000000
21736	1501.750000
21737	1975.924278
21738	1147.750000
21739	2159.189189
21740	2740.043478
21741	5208.384615
21742	2774.192308
21743	5426.357798
21744	1762.300000
21745	3723.102679
21746	5095.288462
21747	5480.642857
21748	2221.702128
21749	2127.458333
21750	1936.157895
21751	3704.967742
21752	1601.923077
21753	2959.166667
21754	1975.924278
21755	1876.560000
21756	3023.941176
21757	1047.875000
21758	1692.000000
21759	2437.733333
21760	2529.428571
21761	3264.567164
21762	1617.370370
21763	1043.666667
21764	1675.024390
21765	1977.400000
21766	2045.791667
21767	2343.833333
21768	3052.857143
21769	1946.105263
21770	1569.941176
21771	1567.275000
21772	6142.914773
21773	1533.894737
21774	2861.492308
21775	1975.924278
21776	1975.924278
21777	1873.052632
21778	2317.320000
21779	1576.871795
21780	2127.526316
21781	1739.800000
21782	1627.857143
21783	2161.800000
21784	4290.953488
21785	1815.545455
21786	1291.833333
21787	1975.924278
21788	1667.800000
21789	1659.769231
21790	4684.423729
21791	2911.758065
21792	4328.139535
21793	4189.979592
21794	1196.200000
21795	1733.982143
21796	2627.120000
21797	1975.924278
21798	1800.666667
21799	1719.187500
21800	4042.833333
21801	2930.863636
21802	1975.924278
21803	1893.333333
21804	1216.559322
21805	2918.100000
21806	1502.250000
21807	5181.209302
21808	1693.263158
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21810	1539.833333
21811	1397.045455
21812	1880.842105
21813	1832.593750
21814	1447.115385
21815	2722.500000
21816	4002.894366
21817	1770.384615
21818	1415.296296
21819	1216.210526
21820	1621.875000
21821	2546.044248
21822	3981.866667
21823	6226.500000
21824	9957.800000
21825	6635.600000
21826	5998.125000
21827	1764.724138
21828	2302.727273
21829	1762.075000
21830	1509.230769
21831	1674.000000
21832	3015.677966
21833	2672.384615
21834	2702.800000
21835	2550.909396
21836	1856.666667
21837	1744.243243
21838	2341.738636
21839	1662.333333
21840	1382.666667
21841	1949.142857
21842	1514.285714
21843	7604.444444
21844	5027.330709
21845	2093.454545
21846	2919.666667
21847	1488.100000
21848	1855.950000
21849	1832.909091
21850	1558.727273
21851	6102.294118
21852	2814.625000
21853	6256.107143
21854	2240.296875
21855	3596.145833
21856	3768.040000
21857	3129.812500
21858	2787.671739
21859	1407.777778
21860	1998.911765
21861	2211.340909
21862	1672.956522
21863	2625.916667
21864	1429.833333
21865	1904.310345
21866	1329.857143
21867	3201.888889
21868	1672.230769
21869	2813.310345
21870	1632.200000
21871	1297.437500
21872	1618.500000
21873	1856.375000
21874	2802.466667
21875	1975.924278
21876	1999.833333
21877	1842.727273
21878	2226.217391
21879	1975.924278
21880	2976.318841
21881	1437.440678
21882	1519.958333
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21884	2325.796610
21885	2729.461538
21886	2560.677419
21887	1975.924278
21888	1650.857143
21889	4932.571429
21890	2315.714286
21891	1833.857143
21892	4065.484848
21893	1675.200000
21894	2342.181818
21895	3505.019802
21896	1755.666667
21897	1824.700000
21898	3698.744186
21899	4085.280374
21900	1573.600000
21901	3575.264368
21902	2120.848485
21903	2240.000000
21904	3176.611111
21905	4359.935484
21906	3510.148649
21907	1141.000000
21908	2122.300000
21909	1968.864865
21910	1686.901961
21911	1942.580645
21912	3244.488372
21913	3087.942857
21914	2607.949495
21915	1887.038462
21916	2584.897436
21917	1942.307692
21918	3720.145833
21919	1605.714286
21920	1975.924278
21921	2438.079208
21922	2737.162791
21923	5649.597765
21924	1975.924278
21925	1672.043478
21926	1352.700000
21927	1504.400000
21928	1536.142857
21929	1975.924278
21930	3161.676923
21931	1829.600000
21932	2410.814815
21933	1951.500000
21934	1724.974359
21935	1575.736842
21936	1106.529412
21937	1774.916667
21938	1257.000000
21939	3395.714286
21940	1175.272727
21941	1604.500000
21942	4873.384615
21943	3253.340206
21944	1764.769231
21945	2168.250000
21946	1367.666667
21947	1125.500000
21948	1438.705882
21949	2077.516129
21950	1874.058824
21951	4561.440678
21952	1521.230769
21953	1972.500000
21954	1680.375000
21955	2585.705882
21956	2770.636364
21957	1779.222222
21958	2135.250000
21959	2001.823529
21960	2058.800000
21961	1971.000000
21962	1840.333333
21963	1108.000000
21964	2370.666667
21965	1500.916667
21966	1653.214286
21967	2645.880952
21968	1955.400000
21969	5271.793103
21970	4957.000000
21971	1414.214286
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21984	1893.451613
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21987	3897.527778
21988	1572.307692
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21990	2678.863636
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21999	1247.700000
22000	1975.924278
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22010	1975.924278
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22019	1975.924278
22020	1975.924278
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22731	1130.769231
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22795	1975.924278
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22813	1975.924278
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22895	1975.924278
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24090	1729.100000
24091	2500.062500
24092	1376.400000
24093	2640.400000
24094	1975.924278
24095	2279.307692
24096	2290.200000
24097	1120.000000
24098	2438.333333
24099	2455.000000
24100	2425.714286
24101	2246.835294
24102	2480.000000
24103	1975.924278
24104	1531.200000
24105	3500.222222
24106	2864.000000
24107	3076.000000
24108	2001.000000
24109	2852.000000
24110	1656.142857
24111	2440.333333
24112	2298.000000
24113	2325.600000
24114	2864.500000
24115	2089.000000
24116	1834.111111
24117	3005.500000
24118	2839.647059
24119	2334.000000
24120	1295.333333
24121	860.000000
24122	1975.924278
24123	2056.333333
24124	2306.940000
24125	2291.250000
24126	2254.000000
24127	1975.924278
24128	1736.400000
24129	1674.000000
24130	1259.400000
24131	3246.650000
24132	2799.666667
24133	2317.000000
24134	1186.500000
24135	2653.521739
24136	2375.307692
24137	1927.250000
24138	1975.924278
24139	2220.272727
24140	1975.924278
24141	1869.846154
24142	1975.924278
24143	2066.000000
24144	2608.000000
24145	2236.000000
24146	2300.500000
24147	2283.720000
24148	2009.493151
24149	2253.125000
24150	1975.924278
24151	2066.000000
24152	1508.333333
24153	2354.000000
24154	1972.428571
24155	1445.100775
24156	1975.924278
24157	3014.500000
24158	1191.600000
24159	1975.924278
24160	1188.000000
24161	2047.285714
24162	1975.924278
24163	2208.250000
24164	1613.000000
24165	1648.428571
24166	3627.170732
24167	2082.111111
24168	1986.000000
24169	1480.666667
24170	1651.600000
24171	3324.750000
24172	1250.000000
24173	2010.250000
24174	2068.666667
24175	2457.761905
24176	2050.400000
24177	2246.500000
24178	3307.500000
24179	2124.333333
24180	1842.000000
24181	1696.000000
24182	1794.857143
24183	1975.924278
24184	3433.853659
24185	2161.809524
24186	1975.924278
24187	2724.000000
24188	2375.833333
24189	3284.125000
24190	1847.333333
24191	1322.500000
24192	2755.175000
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24194	2902.000000
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24197	3474.756757
24198	953.000000
24199	1861.250000
24200	1902.666667
24201	2963.000000
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24205	1339.285714
24206	1663.000000
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24208	2426.114286
24209	1975.924278
24210	1631.777778
24211	1975.924278
24212	1880.000000
24213	1975.924278
24214	2090.000000
24215	2521.066667
24216	1975.924278
24217	1806.466667
24218	1648.600000
24219	1689.333333
24220	2855.500000
24221	1874.066667
24222	2231.000000
24223	2338.214286
24224	2137.000000
24225	2245.500000
24226	2748.500000
24227	4037.000000
24228	1975.924278
24229	1544.400000
24230	4457.500000
24231	3015.200000
24232	2207.666667
24233	2117.666667
24234	2800.000000
24235	2489.500000
24236	2165.500000
24237	2083.000000
24238	2326.875000
24239	1601.000000
24240	1228.500000
24241	1272.500000
24242	2365.000000
24243	2147.000000
24244	3496.333333
24245	2676.000000
24246	2358.214286
24247	2635.166667
24248	1331.250000
24249	1687.000000
24250	1197.500000
24251	1952.700000
24252	2696.000000
24253	1975.924278
24254	2173.153846
24255	1975.924278
24256	1725.000000
24257	1807.200000
24258	2510.727273
24259	2408.888889
24260	1610.666667
24261	1873.571429
24262	2305.833333
24263	3400.666667
24264	1808.900000
24265	1715.500000
24266	2329.000000
24267	2934.156250
24268	1946.714286
24269	2115.500000
24270	2614.062500
24271	1620.750000
24272	2956.750000
24273	2755.500000
24274	2550.416667
24275	1845.214286
24276	1987.000000
24277	1473.000000
24278	1978.666667
24279	1363.666667
24280	2289.111111
24281	2409.272727
24282	929.000000
24283	2819.250000
24284	2615.230769
24285	1975.924278
24286	1622.000000
24287	1366.500000
24288	874.500000
24289	1972.500000
24290	1986.875000
24291	1535.500000
24292	2083.833333
24293	3511.834197
24294	1975.924278
24295	2522.500000
24296	1421.000000
24297	2038.970588
24298	2009.400000
24299	1709.000000
24300	3430.200000
24301	3309.833333
24302	1276.500000
24303	915.000000
24304	1475.900000
24305	1651.750000
24306	1635.000000
24307	2338.000000
24308	1233.000000
24309	1797.153846
24310	1975.924278
24311	2722.750000
24312	3233.428571
24313	2047.750000
24314	1411.500000
24315	1625.250000
24316	2320.333333
24317	2034.756757
24318	2265.500000
24319	2213.800000
24320	2329.400000
24321	1975.924278
24322	1696.296296
24323	2266.214286
24324	1857.846154
24325	2454.894737
24326	1975.924278
24327	3017.250000
24328	1919.400000
24329	1691.250000
24330	1975.924278
24331	2528.200000
24332	2242.625000
24333	2263.466667
24334	1958.000000
24335	2149.800000
24336	1564.625000
24337	2672.333333
24338	1645.250000
24339	2422.363636
24340	1990.000000
24341	2441.000000
24342	3321.833333
24343	1755.750000
24344	2669.000000
24345	1724.500000
24346	2444.000000
24347	3256.076923
24348	2215.910714
24349	2247.333333
24350	2648.000000
24351	2772.600000
24352	2701.000000
24353	658.000000
24354	1500.000000
24355	2374.600000
24356	1749.400000
24357	2191.333333
24358	1738.800000
24359	2817.266667
24360	2281.666667
24361	2727.818182
24362	1662.142857
24363	2587.000000
24364	3929.000000
24365	2662.666667
24366	2682.250000
24367	2865.304348
24368	1062.000000
24369	2272.000000
24370	2182.700000
24371	1975.924278
24372	2215.000000
24373	1134.250000
24374	1688.000000
24375	1975.924278
24376	4017.150000
24377	3194.000000
24378	1347.500000
24379	993.666667
24380	2459.000000
24381	1753.250000
24382	1779.000000
24383	2173.750000
24384	1486.387500
24385	1288.312500
24386	1975.924278
24387	1975.924278
24388	1542.333333
24389	1636.481818
24390	1419.093023
24391	1619.250000
24392	1975.924278
24393	1479.000000
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24395	847.500000
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24399	1975.924278
24400	1975.924278
24401	1576.000000
24402	1975.924278
24403	557.000000
24404	1975.924278
24405	1339.400000
24406	1379.200000
24407	1975.924278
24408	1324.000000
24409	1975.924278
24410	2495.666667
24411	1624.857143
24412	1975.924278
24413	1460.800000
24414	1975.924278
24415	1381.000000
24416	1094.500000
24417	1212.000000
24418	6511.000000
24419	2493.000000
24420	1975.924278
24421	1094.181818
24422	1474.666667
24423	1975.924278
24424	1889.000000
24425	1484.000000
24426	1975.924278
24427	1602.000000
24428	1975.924278
24429	1954.000000
24430	1298.000000
24431	2140.875000
24432	1384.111111
24433	1257.500000
24434	2350.666667
24435	1437.333333
24436	1025.500000
24437	1856.000000
24438	1975.924278
24439	1975.924278
24440	1975.924278
24441	1975.924278
24442	1412.923077
24443	1447.000000
24444	1088.600000
24445	1223.666667
24446	1975.924278
24447	773.000000
24448	2070.750000
24449	1265.000000
24450	1676.500000
24451	1297.200000
24452	1107.000000
24453	1322.500000
24454	1975.924278
24455	860.285714
24456	1118.000000
24457	907.000000
24458	1690.000000
24459	1343.333333
24460	1575.650000
24461	600.000000
24462	1564.857143
24463	1975.924278
24464	1975.924278
24465	1975.924278
24466	1160.000000
24467	1975.924278
24468	1361.600000
24469	1827.000000
24470	2078.750000
24471	1975.924278
24472	997.941176
24473	1616.500000
24474	997.705882
24475	1508.444444
24476	1244.500000
24477	1099.675000
24478	1099.428571
24479	2059.500000
24480	1592.000000
24481	1984.500000
24482	1317.000000
24483	1267.375000
24484	1600.000000
24485	1975.924278
24486	1952.666667
24487	1975.924278
24488	1106.400000
24489	1358.500000
24490	1975.924278
24491	1401.500000
24492	1975.924278
24493	2159.750000
24494	3046.000000
24495	1975.924278
24496	1576.153846
24497	2129.500000
24498	1975.924278
24499	2227.000000
24500	1909.000000
24501	1750.000000
24502	2096.666667
24503	1771.666667
24504	1975.924278
24505	1826.333333
24506	1975.924278
24507	1975.924278
24508	1758.000000
24509	2606.666667
24510	1975.924278
24511	1516.727273
24512	1975.924278
24513	1975.924278
24514	1975.924278
24515	3819.500000
24516	1155.750000
24517	1444.642857
24518	1975.924278
24519	658.500000
24520	1858.448276
24521	1975.924278
24522	1477.595745
24523	1150.000000
24524	1975.924278
24525	1397.500000
24526	879.000000
24527	1474.166667
24528	1186.333333
24529	1975.924278
24530	1298.789474
24531	1975.924278
24532	1975.924278
24533	1244.000000
24534	1975.924278
24535	894.500000
24536	1975.924278
24537	1716.000000
24538	1975.924278
24539	905.500000
24540	1097.086957
24541	1627.750000
24542	858.666667
24543	2040.250000
24544	1592.960784
24545	1122.653061
24546	1383.425287
24547	1694.000000
24548	1975.924278
24549	1975.924278
24550	1975.924278
24551	1975.924278
24552	1975.924278
24553	1365.250000
24554	1975.924278
24555	1975.924278
24556	1975.924278
24557	1298.250000
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24560	1975.924278
24561	1975.924278
24562	1975.924278
24563	1826.250000
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24565	919.333333
24566	1241.000000
24567	1975.924278
24568	2403.500000
24569	1729.833333
24570	1435.333333
24571	1515.400000
24572	1893.608696
24573	1975.924278
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24575	1083.600000
24576	1975.924278
24577	1975.924278
24578	1576.875000
24579	1608.750000
24580	1975.924278
24581	1975.924278
24582	1984.000000
24583	2437.000000
24584	1852.750000
24585	1645.666667
24586	1164.800000
24587	1318.000000
24588	1975.924278
24589	1508.600000
24590	1558.540816
24591	1862.600000
24592	1416.750000
24593	1975.924278
24594	1203.818182
24595	1567.333333
24596	1975.924278
24597	1384.666667
24598	1191.666667
24599	1778.000000
24600	1975.924278
24601	1975.924278
24602	1178.000000
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24605	1805.808511
24606	1975.924278
24607	1357.533333
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24609	1303.875000
24610	1975.924278
24611	1975.924278
24612	1011.444444
24613	1363.000000
24614	1975.924278
24615	1296.125000
24616	1310.750000
24617	1406.500000
24618	1781.222222
24619	2773.000000
24620	1975.924278
24621	1975.924278
24622	1975.924278
24623	1115.652174
24624	1975.924278
24625	2153.000000
24626	2174.666667
24627	1975.924278
24628	1151.615385
24629	1305.400000
24630	1212.500000
24631	1138.625000
24632	1975.924278
24633	1067.833333
24634	1129.000000
24635	2125.000000
24636	656.000000
24637	687.000000
24638	2250.000000
24639	1786.500000
24640	1149.250000
24641	2387.000000
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24643	1975.924278
24644	617.500000
24645	1975.924278
24646	1975.924278
24647	1475.000000
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24649	893.000000
24650	1543.500000
24651	1334.263158
24652	1471.571429
24653	1975.924278
24654	1966.666667
24655	1975.924278
24656	1019.000000
24657	1587.000000
24658	1975.924278
24659	1348.000000
24660	1136.750000
24661	1564.222222
24662	1975.924278
24663	1453.000000
24664	1342.000000
24665	1271.166667
24666	1756.708333
24667	1206.928571
24668	514.000000
24669	1975.924278
24670	1077.666667
24671	1180.250000
24672	1975.924278
24673	2876.333333
24674	1807.000000
24675	1975.924278
24676	1296.285714
24677	1975.924278
24678	1975.924278
24679	1975.924278
24680	1424.000000
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24683	1049.625000
24684	1975.924278
24685	1975.924278
24686	1705.800000
24687	1975.924278
24688	1143.000000
24689	1975.924278
24690	1975.924278
24691	1565.000000
24692	1975.924278
24693	858.500000
24694	1732.666667
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24697	1975.924278
24698	666.666667
24699	2610.800000
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24701	1139.833333
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24703	2348.500000
24704	1975.924278
24705	1285.500000
24706	1975.924278
24707	6429.000000
24708	1975.924278
24709	1975.924278
24710	1552.787879
24711	1975.924278
24712	1771.769231
24713	1479.500000
24714	1975.924278
24715	1508.909091
24716	1569.230769
24717	1807.250000
24718	1038.000000
24719	2894.000000
24720	1386.000000
24721	1884.500000
24722	1667.000000
24723	1172.000000
24724	1975.924278
24725	1975.924278
24726	1975.924278
24727	1975.924278
24728	1548.000000
24729	1012.000000
24730	1975.924278
24731	1975.924278
24732	1975.924278
24733	1392.500000
24734	1975.924278
24735	1975.924278
24736	1857.333333
24737	1057.000000
24738	1183.000000
24739	1975.924278
24740	1293.090909
24741	785.000000
24742	1432.000000
24743	1530.500000
24744	1110.333333
24745	1491.200000
24746	1552.375000
24747	1211.937500
24748	1975.924278
24749	2564.500000
24750	1975.924278
24751	3266.333333
24752	1212.777778
24753	1975.924278
24754	1285.666667
24755	1229.333333
24756	1433.833333
24757	1094.133333
24758	1090.750000
24759	1375.500000
24760	1651.500000
24761	1379.340000
24762	1284.500000
24763	999.333333
24764	1572.250000
24765	2781.666667
24766	1975.924278
24767	1751.705882
24768	1975.924278
24769	1975.924278
24770	1056.333333
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24772	1848.500000
24773	1975.924278
24774	1049.500000
24775	1392.222222
24776	2207.000000
24777	1132.714286
24778	1943.666667
24779	1975.924278
24780	1565.250000
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24783	1630.181818
24784	1682.333333
24785	1718.000000
24786	1521.400000
24787	1115.500000
24788	1287.000000
24789	1278.714286
24790	1603.000000
24791	1325.333333
24792	1351.200000
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24794	1359.500000
24795	1856.500000
24796	1975.924278
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24798	1975.924278
24799	1975.924278
24800	1475.800000
24801	1690.666667
24802	1975.924278
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24805	1975.924278
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24809	1975.924278
24810	5244.500000
24811	1097.000000
24812	1975.924278
24813	1131.571429
24814	2472.000000
24815	1090.636364
24816	1300.666667
24817	1975.924278
24818	1975.924278
24819	1281.500000
24820	1313.000000
24821	790.000000
24822	1690.777778
24823	1975.924278
24824	1487.111111
24825	1427.800000
24826	1975.924278
24827	1355.000000
24828	1071.190476
24829	1975.924278
24830	1585.000000
24831	1617.666667
24832	1196.931818
24833	1975.924278
24834	1381.615385
24835	2744.000000
24836	1975.924278
24837	1975.924278
24838	1975.924278
24839	1975.924278
24840	1942.000000
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24842	1371.217391
24843	1438.142857
24844	2171.000000
24845	1975.924278
24846	1975.924278
24847	2045.000000
24848	1535.000000
24849	3500.000000
24850	926.666667
24851	1865.219048
24852	1293.184211
24853	1061.333333
24854	1181.750000
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24856	1975.924278
24857	1975.924278
24858	2271.500000
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24860	5667.000000
24861	738.500000
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24863	1975.924278
24864	1671.500000
24865	1085.000000
24866	1260.666667
24867	974.500000
24868	1873.800000
24869	968.000000
24870	1168.000000
24871	1975.924278
24872	565.000000
24873	1975.924278
24874	1548.000000
24875	1328.500000
24876	1975.924278
24877	1723.000000
24878	553.000000
24879	1960.055556
24880	554.000000
24881	1593.125000
24882	2162.513158
24883	1541.909091
24884	987.500000
24885	2259.000000
24886	1472.750000
24887	1477.000000
24888	2061.500000
24889	1981.333333
24890	1768.250000
24891	1921.166667
24892	1452.206522
24893	1802.000000
24894	1386.000000
24895	1184.000000
24896	1751.250000
24897	1942.314286
24898	2126.714286
24899	1774.428571
24900	1668.333333
24901	1468.322581
24902	1429.000000
24903	4112.400000
24904	1975.924278
24905	1975.924278
24906	1729.500000
24907	1033.000000
24908	811.000000
24909	1941.538462
24910	1951.551724
24911	1661.709677
24912	1763.500000
24913	1560.384615
24914	1122.833333
24915	2402.633333
24916	1840.782609
24917	2089.125000
24918	1710.728261
24919	2420.326000
24920	1844.800000
24921	2216.916667
24922	2147.000000
24923	1506.142857
24924	1335.000000
24925	1975.924278
24926	1381.524272
24927	1795.400000
24928	1103.307692
24929	2029.195652
24930	3111.333333
24931	1080.333333
24932	3710.636364
24933	1916.444444
24934	4278.666667
24935	2038.750000
24936	1310.500000
24937	1091.000000
24938	1081.304348
24939	6829.200000
24940	1738.000000
24941	1753.111111
24942	1527.250000
24943	1516.655405
24944	1975.924278
24945	1957.600000
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25784	1975.924278
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25790	1975.924278
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26571	1975.924278
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26612	1975.924278
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26634	1975.924278
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26650	1096.000000
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26652	1975.924278
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26660	2983.500000
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26667	1946.500000
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26672	1975.924278
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26680	2791.533333
26681	1975.924278
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26686	1975.924278
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26689	1975.924278
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26698	1975.924278
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26705	1975.924278
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26710	2467.400000
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26714	1976.000000
26715	6638.419672
26716	2867.272727
26717	2494.666667
26718	1975.924278
26719	1975.924278
26720	2204.666667
26721	2662.500000
26722	2082.666667
26723	1398.000000
26724	1996.500000
26725	1893.176471
26726	2685.666667
26727	4265.210526
26728	3969.897959
26729	1975.924278
26730	2045.305882
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26736	1975.924278
26737	1398.333333
26738	1016.666667
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26742	2558.724138
26743	1975.924278
26744	2296.529412
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26760	2009.444444
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26770	1975.924278
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26775	1975.924278
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26789	2139.375000
26790	1975.924278
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26795	1951.250000
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26801	834.500000
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26816	1975.924278
26817	1158.500000
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26825	1975.924278
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26827	1975.924278
26828	1328.315789
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26847	2006.969697
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26890	1975.924278
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26895	1975.924278
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26905	1975.924278
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26916	1975.924278
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26919	1975.924278
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26922	1975.924278
26923	1975.924278
26924	1975.924278
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26927	1975.924278
26928	1975.924278
26929	2500.000000
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26949	1975.924278
26950	1975.924278
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26965	1975.924278
26966	1975.924278
26967	1975.924278
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26975	1975.924278
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26983	1975.924278
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27001	1975.924278
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27070	1975.924278
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27082	1975.924278
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27093	1975.924278
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27116	1975.924278
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27125	1975.924278
27126	1975.924278
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27129	1975.924278
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27139	1975.924278
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27163	1975.924278
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27185	1975.924278
27186	1975.924278
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27197	1975.924278
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27202	1975.924278
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27205	1975.924278
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27225	1975.924278
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27235	1975.924278
27236	1975.924278
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27239	1975.924278
27240	1975.924278
27241	1975.924278
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27243	1975.924278
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27251	1975.924278
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27254	1975.924278
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27257	1975.924278
27258	1975.924278
27259	1975.924278
27260	1975.924278
27261	1975.924278
27262	1975.924278
27263	2621.785714
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27293	1975.924278
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27303	1975.924278
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27306	1975.924278
27307	2683.800000
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27311	2446.793103
27312	2593.895161
27313	2377.800000
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27315	2805.666667
27316	5057.259615
27317	1975.924278
27318	2126.636364
27319	2169.923077
27320	1520.285714
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27330	3160.600000
27331	1636.750000
27332	2009.023256
27333	1131.142857
27334	1388.700000
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27336	1948.200000
27337	2123.666667
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27339	1975.924278
27340	1975.924278
27341	1832.333333
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27344	2559.500000
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27348	1075.000000
27349	1975.924278
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27356	3124.700000
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27358	1283.176471
27359	2581.500000
27360	2448.250000
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27362	1971.666667
27363	2509.869565
27364	1975.924278
27365	1636.444444
27366	2176.357143
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27368	2539.809524
27369	1258.833333
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27375	2553.470588
27376	3096.750000
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27392	3758.533333
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27394	1693.090909
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27399	2516.822222
27400	2429.079365
27401	2205.500000
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27405	2372.500000
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27408	1638.210084
27409	1799.766667
27410	1975.924278
27411	1975.924278
27412	1044.500000
27413	3176.347826
27414	1390.000000
27415	960.000000
27416	1975.924278
27417	1644.000000
27418	2117.666667
27419	3136.437500
27420	1624.454545
27421	2442.600000
27422	2172.666667
27423	1596.076923
27424	1364.142857
27425	2898.970588
27426	2899.394737
27427	1310.000000
27428	1975.924278
27429	4082.079602
27430	2430.924138
27431	2320.777778
27432	2223.710526
27433	2939.170213
27434	2136.710526
27435	2464.937500
27436	2509.700000
27437	1405.259259
27438	2276.122222
27439	4219.588235
27440	3593.342857
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27445	1189.900000
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27454	3251.015625
27455	1975.924278
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27458	1975.924278
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27460	794.000000
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27462	2075.500000
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27464	1800.307692
27465	2461.450000
27466	1975.924278
27467	2410.123810
27468	3217.666667
27469	3166.500000
27470	2591.595238
27471	2621.083333
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27473	1370.000000
27474	1992.700000
27475	2555.600000
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27477	2238.500000
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27484	3011.567568
27485	2113.500000
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27488	622.500000
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27490	749.000000
27491	1975.924278
27492	1975.924278
27493	1975.924278
27494	1975.924278
27495	1975.924278
27496	1975.924278
27497	1975.924278
27498	1975.924278
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28408	1975.924278
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28410	2667.666667
28411	1635.208333
28412	2628.300699
28413	3137.000000
28414	2411.384615
28415	1940.000000
28416	3091.090909
28417	3423.139535
28418	3138.666667
28419	4235.663462
28420	4626.551020
28421	3362.294900
28422	2243.400000
28423	2678.200000
28424	4125.434783
28425	4590.961538
28426	4472.236641
28427	1861.600000
28428	3107.250000
28429	2488.555556
28430	1310.714286
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28434	4645.935065
28435	4954.385965
28436	2764.000000
28437	4373.179487
28438	3321.600000
28439	1975.924278
28440	3814.678571
28441	3673.218750
28442	3137.625000
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28445	1845.857143
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28460	4654.200000
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28464	1975.924278
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28468	2940.714286
28469	2461.200000
28470	3063.352941
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28475	4612.416667
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28477	4219.714286
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28479	4233.813187
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28490	3981.928571
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28501	2973.500000
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28510	2289.833333
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28513	4888.270798
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28515	4711.100000
28516	3163.600000
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28522	9212.000000
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28529	3864.947368
28530	3432.611111
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28532	2709.833333
28533	1975.924278
28534	1975.924278
28535	3691.821429
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28537	1975.924278
28538	2270.142857
28539	3425.702703
28540	5302.900000
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28542	2810.500000
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28544	3130.695652
28545	1975.924278
28546	3475.157895
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28560	2955.846154
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28568	1975.924278
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28571	2477.625000
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28574	1975.924278
28575	2111.727273
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28577	4329.769231
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28582	1975.924278
28583	2123.500000
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28585	2266.166667
28586	2522.881481
28587	2921.882353
28588	3024.222222
28589	4323.550201
28590	1969.100000
28591	4178.704433
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28595	3453.277778
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28597	1975.924278
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28599	4360.200000
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28601	1974.777778
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28608	1975.924278
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28610	1975.924278
28611	2746.000000
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28613	3106.341463
28614	1336.571429
28615	1975.924278
28616	2992.600000
28617	5765.636364
28618	2900.230769
28619	3253.766667
28620	2693.800000
28621	1975.924278
28622	2395.413043
28623	3217.562500
28624	1949.833333
28625	4308.346154
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28627	1834.898438
28628	4657.219931
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28630	4154.428571
28631	1975.924278
28632	1550.264706
28633	3855.303030
28634	4751.440000
28635	1975.924278
28636	2915.000000
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28638	2735.333333
28639	3943.627907
28640	3128.673913
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28645	1975.924278
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28650	2761.250000
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28660	3704.487805
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28664	3991.138408
28665	4103.000000
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28667	4246.842105
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28669	4247.084746
28670	3902.285714
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28675	5017.384615
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28680	3999.157895
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28682	1975.924278
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28685	1975.924278
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28688	1975.924278
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28712	1975.924278
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28717	1975.924278
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28719	1975.924278
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28721	1127.875000
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28736	1975.924278
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28739	1975.924278
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28765	1622.666667
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28827	1228.333333
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28864	1975.924278
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28870	1975.924278
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28937	1975.924278
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28944	1975.924278
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28964	1975.924278
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28971	1975.924278
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28974	1975.924278
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28984	1975.924278
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28986	1975.924278
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28989	1975.924278
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29038	593.000000
29039	1975.924278
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29055	1975.924278
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29057	1975.924278
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29072	1975.924278
29073	1975.924278
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29078	1975.924278
29079	604.500000
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29084	1975.924278
29085	2174.000000
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29090	937.000000
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29095	1147.333333
29096	1975.924278
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29119	1975.924278
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29123	1975.924278
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29127	1975.924278
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29136	1975.924278
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29152	1975.924278
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29198	1975.924278
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29201	1975.924278
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29252	1975.924278
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30114	1322.857143
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30120	1200.333333
30121	2369.585366
30122	2023.733333
30123	1635.500000
30124	1535.500000
30125	853.750000
30126	980.222222
30127	1014.071429
30128	1809.461538
30129	1512.111111
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30139	1723.529412
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30149	1233.833333
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30151	1560.166667
30152	1497.750000
30153	2374.545455
30154	1294.714286
30155	1975.924278
30156	1606.615385
30157	1329.384615
30158	1055.375000
30159	1180.285714
30160	897.571429
30161	1329.750000
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30173	1594.714286
30174	1453.000000
30175	1975.924278
30176	1654.914286
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30179	1975.924278
30180	2878.843750
30181	2339.230769
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30184	2606.266667
30185	4513.352745
30186	3648.816667
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30188	4028.500000
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30190	3127.000000
30191	10169.801587
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30193	2917.500000
30194	1860.250000
30195	3691.800000
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30200	2715.555556
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30206	3791.500000
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30211	1975.924278
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30216	3916.961538
30217	1975.924278
30218	1975.924278
30219	7594.750000
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30225	2969.800000
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30228	1975.924278
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30230	2386.950000
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30232	2324.333333
30233	1975.924278
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30236	3159.138015
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30241	5536.689655
30242	1975.924278
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30250	1975.924278
30251	3300.538462
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30255	3246.409091
30256	2196.333333
30257	3145.888889
30258	5375.857143
30259	2531.818182
30260	1809.666667
30261	7870.000000
30262	2492.333333
30263	1975.924278
30264	6850.785714
30265	2330.250000
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30267	4212.166667
30268	4832.379310
30269	4251.000000
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30271	1549.000000
30272	4602.718750
30273	2047.636364
30274	1769.923077
30275	1273.333333
30276	2092.714286
30277	1975.924278
30278	1978.625000
30279	2076.333333
30280	1975.924278
30281	1975.924278
30282	1975.924278
30283	3832.952381
30284	1975.924278
30285	3540.164384
30286	1047.142857
30287	1975.924278
30288	1649.000000
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30292	4791.944444
30293	2812.821429
30294	1975.924278
30295	1975.924278
30296	4046.794118
30297	2571.882353
30298	2560.312500
30299	2685.200000
30300	7107.176471
30301	3326.333333
30302	2036.055556
30303	4031.222222
30304	3639.800000
30305	4102.500000
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30309	1975.924278
30310	1975.924278
30311	3463.666667
30312	3275.000000
30313	2654.000000
30314	2463.500000
30315	4932.583916
30316	3860.206897
30317	3237.333333
30318	2997.666667
30319	3661.000000
30320	4568.272727
30321	1975.924278
30322	2158.970000
30323	1975.924278
30324	2028.750000
30325	3820.250000
30326	2514.200000
30327	2804.000000
30328	1975.924278
30329	4155.166667
30330	1975.924278
30331	1975.924278
30332	1874.600000
30333	1975.924278
30334	1640.500000
30335	2566.547619
30336	1476.800000
30337	2508.500000
30338	5708.430769
30339	3612.000000
30340	2335.333333
30341	3762.990476
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30344	4898.666667
30345	4346.000000
30346	3819.333333
30347	5239.142857
30348	2550.441176
30349	1980.200000
30350	2954.800000
30351	2655.100000
30352	3090.875000
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30354	3142.000000
30355	2631.676471
30356	2830.750000
30357	5453.689655
30358	1975.924278
30359	3305.200000
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30362	1975.924278
30363	1920.551724
30364	1975.924278
30365	5370.478261
30366	2507.333333
30367	4631.500000
30368	2972.000000
30369	3977.875000
30370	1956.150000
30371	3657.480000
30372	2908.750000
30373	2380.960784
30374	2271.571429
30375	3124.400000
30376	2568.333333
30377	2964.800000
30378	3128.166667
30379	4279.500000
30380	3837.016129
30381	2013.100000
30382	2599.357143
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30384	2178.642857
30385	10135.712963
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30389	2122.416667
30390	6177.961538
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30392	2511.333333
30393	3464.540541
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30395	2869.400000
30396	1975.924278
30397	1975.924278
30398	2107.000000
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30400	2751.347826
30401	4038.200000
30402	1975.924278
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30408	1909.500000
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30413	857.500000
30414	7555.784768
30415	2113.800000
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30417	1975.924278
30418	2185.033333
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30421	3193.666667
30422	1975.924278
30423	2657.800000
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30427	1975.924278
30428	1503.000000
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30433	1975.924278
30434	2085.500000
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30437	3159.454545
30438	3080.500000
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30440	3911.379310
30441	3524.647059
30442	1975.924278
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30455	2545.500000
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30457	12042.641975
30458	1975.924278
30459	5042.133333
30460	3460.852941
30461	3506.200000
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30469	1975.924278
30470	5406.764706
30471	2579.875000
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30473	1975.924278
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30480	4166.818182
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30490	4540.777778
30491	5480.978750
30492	1975.924278
30493	3470.046429
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30495	4995.882716
30496	4248.200000
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30499	6078.840000
30500	3787.166667
30501	3700.833333
30502	1975.924278
30503	4558.727273
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30510	2830.394737
30511	3729.803279
30512	2840.404255
30513	3191.000000
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30521	5004.642857
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30536	1975.924278
30537	5135.500000
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30540	3423.444444
30541	3463.500000
30542	3997.000000
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30546	4872.018868
30547	1975.924278
30548	5083.500000
30549	3643.875000
30550	4330.600000
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30560	1975.924278
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30566	3435.111111
30567	8238.250000
30568	1975.924278
30569	4995.287879
30570	1975.924278
30571	4639.296296
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30575	3293.500000
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30577	5888.000000
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30580	4338.023256
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30583	6069.571429
30584	3515.800000
30585	1975.924278
30586	3393.500000
30587	5405.523077
30588	7878.333333
30589	4337.700000
30590	4679.714286
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30592	4532.198980
30593	1975.924278
30594	5117.687500
30595	2953.833333
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30597	4726.842105
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30599	4456.937500
30600	4714.710526
30601	2036.000000
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30605	7664.744681
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30608	5216.500000
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30610	3852.666667
30611	3134.578947
30612	4168.733333
30613	4480.100000
30614	7372.478261
30615	4298.625000
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30617	4573.125000
30618	5570.533333
30619	3928.500000
30620	4254.888889
30621	4044.800000
30622	4639.384615
30623	5423.809524
30624	3944.450000
30625	3581.133333
30626	3953.375000
30627	3942.133333
30628	4474.923077
30629	3410.588235
30630	5904.795455
30631	4384.833333
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30633	4433.681818
30634	3716.085106
30635	3610.000000
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30637	3228.000000
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30639	3378.075000
30640	4611.333333
30641	4277.437500
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30650	5190.166667
30651	1975.924278
30652	1975.924278
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30657	1975.924278
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30660	4276.894737
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30664	4495.714286
30665	4174.500000
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30667	3727.030303
30668	4973.800000
30669	4367.333333
30670	4682.306122
30671	4971.869565
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30673	1403.600000
30674	1975.924278
30675	4112.312500
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30678	4392.416667
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30680	3811.666667
30681	4459.090909
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30684	5048.800000
30685	1975.924278
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30688	4032.684932
30689	3705.750000
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30691	2615.500000
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30694	3796.500000
30695	4581.500000
30696	4226.071429
30697	3277.500000
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30699	3657.200000
30700	4731.333333
30701	4492.285714
30702	5475.946721
30703	3918.153846
30704	4006.537037
30705	6106.392857
30706	4023.285714
30707	3048.615385
30708	6412.597403
30709	4755.207865
30710	4279.125000
30711	1975.924278
30712	4547.214286
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30714	5493.000000
30715	3619.000000
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30717	3870.400000
30718	3761.814346
30719	3870.800000
30720	5352.792683
30721	3994.200000
30722	3923.666667
30723	2938.666667
30724	3923.000000
30725	4707.900000
30726	2795.406250
30727	4657.857143
30728	6647.818182
30729	6958.480769
30730	1975.924278
30731	2719.583333
30732	1975.924278
30733	3970.428571
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30735	3991.750000
30736	1975.924278
30737	6555.714286
30738	4057.633803
30739	3249.101449
30740	3793.454545
30741	4731.903226
30742	3901.746114
30743	4732.455882
30744	3635.200000
30745	2778.818182
30746	3768.200000
30747	3679.100000
30748	3264.111111
30749	4278.626667
30750	3874.928571
30751	5572.117647
30752	3999.800000
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30754	4431.637681
30755	5215.137931
30756	1111.500000
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30760	10840.000000
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30762	5903.833333
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30764	3407.714286
30765	4133.288889
30766	3575.000000
30767	4264.833333
30768	3951.333333
30769	4578.417910
30770	5342.142857
30771	3952.846154
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30775	4343.800000
30776	10200.964769
30777	1975.924278
30778	1975.924278
30779	1975.924278
30780	1975.924278
30781	1975.924278
30782	1975.924278
30783	1975.924278
30784	1975.924278
30785	1975.924278
30786	1975.924278
30787	1975.924278
30788	1975.924278
30789	1975.924278
30790	1975.924278
30791	1975.924278
30792	1975.924278
30793	1975.924278
30794	1975.924278
30795	1975.924278
30796	1975.924278
30797	1880.125000
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30799	1983.000000
30800	2139.735849
30801	1698.000000
30802	2042.714286
30803	1823.400000
30804	1781.500000
30805	1767.666667
30806	1864.333333
30807	1341.500000
30808	1968.200000
30809	2786.307692
30810	1964.583333
30811	2542.500000
30812	2013.500000
30813	1896.923077
30814	2212.250000
30815	2175.900000
30816	2132.833333
30817	1726.571429
30818	1288.200000
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30820	1845.666667
30821	1875.034483
30822	1975.924278
30823	1975.924278
30824	619.500000
30825	2358.800000
30826	1975.924278
30827	2289.000000
30828	1996.000000
30829	1965.600000
30830	1354.714286
30831	1900.000000
30832	1975.924278
30833	2292.250000
30834	2120.071429
30835	2008.500000
30836	2480.555556
30837	1353.500000
30838	1943.300000
30839	1975.924278
30840	2520.000000
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30843	2092.200000
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30845	2207.181818
30846	2050.388889
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30860	2282.750000
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30864	1359.888889
30865	1456.714286
30866	1893.000000
30867	1975.924278
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30870	2139.400000
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30872	1975.924278
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30874	1975.924278
30875	1975.924278
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30877	952.000000
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30880	2400.000000
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30883	1975.924278
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30890	1825.000000
30891	1975.924278
30892	1975.924278
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30895	1633.958333
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30901	3244.044335
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30903	1975.924278
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30905	1975.924278
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30910	3068.857143
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30912	2076.000000
30913	2723.333333
30914	1348.428571
30915	1858.500000
30916	2554.200000
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30918	1948.739130
30919	1975.924278
30920	1975.924278
30921	1804.250000
30922	1540.000000
30923	1271.000000
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31787	3161.560000
31788	2003.750000
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31800	4733.520000
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31803	1975.924278
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31831	1975.924278
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31877	1975.924278
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31900	4115.427778
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31914	2656.066667
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31922	1975.924278
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31925	1975.924278
31926	3350.952381
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31928	1366.750000
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31930	1973.473684
31931	2182.700000
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31943	1975.924278
31944	1748.500000
31945	2441.264706
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31948	1847.625000
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31950	4016.800000
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31960	4127.625000
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31998	1926.250000
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32000	3134.964286
32001	2221.400000
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32015	1975.924278
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32020	1369.500000
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32061	1975.924278
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32065	4011.100000
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32069	3320.285714
32070	4880.078947
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32082	1975.924278
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32097	3285.043478
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32100	3535.166667
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32144	1975.924278
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32251	1975.924278
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32299	3750.152174
32300	5018.549133
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32311	4118.158730
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32317	930.066667
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32320	1975.924278
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32325	1780.428571
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32327	1975.924278
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32340	1975.924278
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32345	1975.924278
32346	1973.363636
32347	1975.924278
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32349	1975.924278
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32352	1975.924278
32353	1975.924278
32354	1975.924278
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32360	1975.924278
32361	1975.924278
32362	1975.924278
32363	1975.924278
32364	1291.125000
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32374	1975.924278
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32380	1975.924278
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32389	1975.924278
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32399	1318.260870
32400	1975.924278
32401	1975.924278
32402	2008.483871
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32409	1975.924278
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32412	969.666667
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32414	1975.924278
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32423	993.571429
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32442	1975.924278
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32447	1975.924278
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32460	652.000000
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32464	1975.924278
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32466	1975.924278
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32468	1975.924278
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32473	1975.924278
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32477	1089.880000
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32479	1975.924278
32480	1975.924278
32481	1550.739130
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32485	1024.358974
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32488	1975.924278
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32499	1975.924278
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32502	1975.924278
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32507	1975.924278
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32509	1975.924278
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32512	1198.571429
32513	1305.333333
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32515	1785.500000
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32520	1975.924278
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32526	1975.924278
32527	2010.304348
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32538	1975.924278
32539	1975.924278
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32541	1975.924278
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32553	1975.924278
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32555	1956.153846
32556	1975.924278
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32560	983.833333
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32563	1975.924278
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32565	1142.500000
32566	1401.166667
32567	972.857143
32568	1835.909091
32569	1080.451613
32570	1063.117647
32571	1319.333333
32572	1437.615385
32573	705.400000
32574	2126.166667
32575	2056.000000
32576	1165.138889
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32590	1975.924278
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33050	1975.924278
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33099	1975.924278
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33117	1975.924278
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33121	1975.924278
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33163	1975.924278
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33189	1975.924278
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33192	1975.924278
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33220	1975.924278
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33254	1975.924278
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33260	1975.924278
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33263	1975.924278
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33283	1975.924278
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33293	1987.714286
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33303	1975.924278
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33309	1975.924278
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33311	1975.924278
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33314	1975.924278
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33316	1975.924278
33317	1997.800000
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33321	1975.924278
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33330	1554.800000
33331	1526.750000
33332	1975.924278
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33341	1975.924278
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33352	1975.924278
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33356	1975.924278
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33359	1975.924278
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33363	1975.924278
33364	1387.500000
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33381	1975.924278
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33391	1975.924278
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33400	1975.924278
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33408	1975.924278
33409	1975.924278
33410	1975.924278
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33420	1975.924278
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33426	1975.924278
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33430	1975.924278
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33439	2253.954545
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34269	1975.924278
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34280	1975.924278
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34299	2067.777778
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34302	1991.750000
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34307	1975.924278
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34318	1988.066667
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34323	1975.924278
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34410	2120.076923
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34421	1975.924278
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34429	1808.636364
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34460	1975.924278
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34667	1975.924278
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34748	1975.924278
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34765	1975.924278
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34801	925.000000
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34810	1451.500000
34811	1975.924278
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34840	1975.924278
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34850	2106.672727
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34870	1798.178030
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35096	1975.924278
35097	873.666667
35098	1050.310345
35099	1892.600000
35100	1609.033333
35101	1975.924278
35102	1090.600000
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35109	1975.924278
35110	1242.545455
35111	810.000000
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35113	1975.924278
35114	2815.000000
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35121	1975.924278
35122	810.500000
35123	1975.924278
35124	1481.500000
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35128	1975.924278
35129	1505.333333
35130	1325.666667
35131	1989.000000
35132	1629.352941
35133	1975.924278
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35136	1975.924278
35137	729.000000
35138	1975.924278
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35140	1975.924278
35141	2066.400000
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35144	1516.616368
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35148	1975.924278
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35152	1975.924278
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35155	1975.924278
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35158	1975.924278
35159	1361.500000
35160	791.181818
35161	2094.714286
35162	1960.210526
35163	904.000000
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35167	1975.924278
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35169	1975.924278
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35171	1975.924278
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35173	1975.924278
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35176	1975.924278
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35182	1975.924278
35183	1975.924278
35184	1975.924278
35185	1900.000000
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35187	1975.924278
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35192	1975.924278
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35200	1975.924278
35201	1975.924278
35202	1975.924278
35203	814.500000
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35207	1975.924278
35208	1975.924278
35209	1975.924278
35210	1975.924278
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35214	1975.924278
35215	1975.924278
35216	1241.800000
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35218	897.600000
35219	1975.924278
35220	1958.500000
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35229	1975.924278
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35235	1975.924278
35236	1895.400000
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35245	1975.924278
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35251	1371.666667
35252	536.500000
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35258	1975.924278
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35260	1975.924278
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35262	1975.924278
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35266	1975.924278
35267	1975.924278
35268	1184.250000
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35275	1975.924278
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35278	1975.924278
35279	1975.924278
35280	1046.666667
35281	1374.571429
35282	1975.924278
35283	1145.853659
35284	1975.924278
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35286	1975.924278
35287	900.000000
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35290	1975.924278
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35293	1975.924278
35294	1541.500000
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35297	1975.924278
35298	1236.031250
35299	1601.200000
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35302	999.000000
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35306	1975.924278
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35309	1975.924278
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35313	1975.924278
35314	1975.924278
35315	765.000000
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35317	1611.666667
35318	1975.924278
35319	1458.500000
35320	1975.924278
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35332	1004.750000
35333	892.875000
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35336	1975.924278
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35340	1975.924278
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35349	1975.924278
35350	1975.924278
35351	940.500000
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35354	1975.924278
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35356	1975.924278
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35358	1975.924278
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35362	1975.924278
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35364	1975.924278
35365	893.250000
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35367	1975.924278
35368	806.000000
35369	1975.924278
35370	995.666667
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35377	1975.924278
35378	1975.924278
35379	868.666667
35380	1470.730769
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35392	2065.777778
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35396	1975.924278
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35398	1892.400000
35399	1240.666667
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35401	1780.000000
35402	1975.924278
35403	1975.924278
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35405	1975.924278
35406	1975.924278
35407	1388.833333
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35409	1975.924278
35410	1500.000000
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35413	1246.400000
35414	1593.375000
35415	1371.500000
35416	1741.560000
35417	1975.924278
35418	1471.000000
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35421	1975.924278
35422	759.500000
35423	1975.924278
35424	1975.924278
35425	580.000000
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35428	1975.924278
35429	1457.555556
35430	650.000000
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35433	1921.333333
35434	1434.733333
35435	1975.924278
35436	1437.428571
35437	1478.730769
35438	1975.924278
35439	2669.047619
35440	3782.000000
35441	1975.924278
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35443	1975.924278
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35446	1975.924278
35447	1975.924278
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35453	1239.769231
35454	1975.924278
35455	1941.529412
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35458	1975.924278
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35460	1975.924278
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35464	1975.924278
35465	1975.924278
35466	1821.333333
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35469	2645.400000
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35475	1000.000000
35476	792.000000
35477	945.714286
35478	1975.924278
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35485	1410.447368
35486	1975.924278
35487	1975.924278
35488	1265.000000
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35496	1975.924278
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35500	3442.972973
35501	1975.924278
35502	1975.924278
35503	885.500000
35504	1549.307692
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35510	627.666667
35511	942.500000
35512	722.000000
35513	1975.924278
35514	1640.466667
35515	904.800000
35516	1028.666667
35517	434.500000
35518	1187.333333
35519	1863.000000
35520	927.250000
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35522	1606.333333
35523	1975.924278
35524	1763.454545
35525	1814.359023
35526	1498.731707
35527	1167.000000
35528	1975.924278
35529	1708.333333
35530	1319.500000
35531	1606.200000
35532	1975.924278
35533	1285.000000
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35535	1305.000000
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35538	1280.000000
35539	673.000000
35540	1155.666667
35541	1448.333333
35542	1252.500000
35543	1975.924278
35544	1282.500000
35545	1335.250000
35546	1113.105263
35547	1358.500000
35548	671.000000
35549	1495.615385
35550	1512.250000
35551	3240.200000
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35560	1058.000000
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35562	1975.924278
35563	1975.924278
35564	1247.857143
35565	1346.565217
35566	2373.769231
35567	917.800000
35568	1975.924278
35569	1168.500000
35570	1435.714286
35571	1617.708333
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35575	1696.785714
35576	1975.924278
35577	1641.200000
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35579	1975.924278
35580	1811.000000
35581	1193.000000
35582	1791.000000
35583	1371.171717
35584	978.000000
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35586	1204.000000
35587	854.727273
35588	1528.666667
35589	1937.888889
35590	2180.333333
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35593	1975.924278
35594	1975.924278
35595	1269.846154
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35599	1975.924278
35600	1291.000000
35601	1187.000000
35602	1975.924278
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35604	1975.924278
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35610	1257.000000
35611	988.200000
35612	902.125000
35613	846.250000
35614	1208.125000
35615	1000.000000
35616	1775.058824
35617	1092.000000
35618	1975.924278
35619	1868.250000
35620	1237.166667
35621	1104.833333
35622	1303.904762
35623	878.833333
35624	700.000000
35625	1425.500000
35626	1130.500000
35627	2363.250000
35628	1067.000000
35629	1975.924278
35630	1165.071429
35631	586.000000
35632	1448.227273
35633	1992.000000
35634	1839.571429
35635	1291.750000
35636	1250.000000
35637	1044.000000
35638	902.000000
35639	774.750000
35640	1643.000000
35641	1293.965517
35642	1420.142857
35643	2121.000000
35644	1970.666667
35645	1667.000000
35646	1194.600000
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35650	1208.666667
35651	1791.000000
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35653	577.000000
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35655	1349.416667
35656	1975.924278
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35660	1671.142857
35661	1271.555556
35662	1975.924278
35663	842.000000
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35665	873.000000
35666	1975.924278
35667	1038.142857
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35669	1004.000000
35670	574.500000
35671	1091.500000
35672	1975.924278
35673	1421.666667
35674	741.000000
35675	845.000000
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35678	1975.924278
35679	1975.924278
35680	1194.500000
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35682	1559.937500
35683	1317.400000
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35685	1761.000000
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35688	1846.333333
35689	1975.924278
35690	1205.100000
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35699	1975.924278
35700	1712.500000
35701	936.000000
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35705	1036.000000
35706	781.333333
35707	1032.285714
35708	1143.750000
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35710	758.000000
35711	1975.924278
35712	456.000000
35713	1532.285714
35714	1284.076923
35715	1840.500000
35716	1550.333333
35717	1254.666667
35718	793.000000
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35720	1515.166667
35721	1755.437500
35722	981.000000
35723	1027.250000
35724	2439.588235
35725	1754.500000
35726	1975.924278
35727	1456.333333
35728	1975.924278
35729	1975.924278
35730	1653.916667
35731	1143.800000
35732	773.500000
35733	1975.924278
35734	931.000000
35735	1012.000000
35736	1320.187500
35737	930.666667
35738	764.000000
35739	2135.727273
35740	2185.714286
35741	1527.000000
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35743	632.000000
35744	1975.924278
35745	1455.000000
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35747	2122.540541
35748	1848.000000
35749	1941.222222
35750	718.666667
35751	1975.924278
35752	1436.375000
35753	1975.924278
35754	1188.000000
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35758	1975.924278
35759	1078.846154
35760	964.333333
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35764	1568.142857
35765	1356.500000
35766	1039.888889
35767	861.000000
35768	1588.800000
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35770	1646.730769
35771	851.000000
35772	1219.200000
35773	1975.924278
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35775	928.166667
35776	1975.924278
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35780	1246.666667
35781	830.500000
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35784	796.000000
35785	1135.500000
35786	1392.600000
35787	1261.750000
35788	1975.924278
35789	1547.363636
35790	1975.924278
35791	1408.700000
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35795	1321.750000
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35797	1975.924278
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35800	1975.924278
35801	941.333333
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35809	1975.924278
35810	1392.571429
35811	1667.000000
35812	1975.924278
35813	1272.833333
35814	1482.300000
35815	833.000000
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35817	2234.050000
35818	1156.333333
35819	1975.924278
35820	1393.200000
35821	2134.428571
35822	1975.924278
35823	1383.117647
35824	860.135135
35825	1827.622642
35826	1365.333333
35827	1545.466667
35828	1208.300000
35829	1298.000000
35830	1403.666667
35831	2469.500000
35832	1036.000000
35833	1629.857143
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35835	1975.924278
35836	1975.924278
35837	1223.666667
35838	2294.857143
35839	1243.000000
35840	991.600000
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35842	1262.250000
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35844	1975.924278
35845	1447.285714
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35847	1822.333333
35848	2042.615385
35849	905.500000
35850	1568.600000
35851	1975.924278
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35854	1975.924278
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35858	1975.924278
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35860	1844.000000
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35862	1975.924278
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35864	1869.571014
35865	905.444444
35866	1360.571429
35867	1579.277778
35868	1291.833333
35869	878.333333
35870	1158.666667
35871	1386.600000
35872	1982.833333
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35874	1854.692308
35875	1152.875000
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35877	1050.000000
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35880	867.000000
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35884	1975.924278
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35887	1975.924278
35888	1301.062500
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35890	921.125000
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35893	1598.151515
35894	1109.277778
35895	1975.924278
35896	1975.924278
35897	1975.924278
35898	1222.000000
35899	1044.428571
35900	1160.500000
35901	1195.500000
35902	1969.800000
35903	1975.924278
35904	1975.924278
35905	1701.000000
35906	2002.375000
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35910	1304.000000
35911	1875.333333
35912	1294.272727
35913	1269.500000
35914	935.875000
35915	1587.200000
35916	1823.700000
35917	1975.924278
35918	3256.250000
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35920	1975.924278
35921	2016.058824
35922	1533.777778
35923	1366.500000
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35925	1556.777778
35926	1975.924278
35927	1420.833333
35928	1975.924278
35929	1975.924278
35930	1707.909091
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35932	953.200000
35933	1679.250000
35934	1975.924278
35935	1562.921053
35936	1963.666667
35937	1512.500000
35938	1662.900000
35939	1277.875000
35940	1356.307692
35941	534.000000
35942	1843.500000
35943	1237.571429
35944	1975.924278
35945	1411.000000
35946	784.500000
35947	1321.500000
35948	1388.875000
35949	1525.800000
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35951	938.000000
35952	2000.000000
35953	1327.000000
35954	1975.924278
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35956	1020.000000
35957	1288.250000
35958	1729.750000
35959	2259.000000
35960	1804.500000
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35966	1375.878165
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36273	9314.682927
36274	7147.436681
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36601	1975.924278
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36610	1975.924278
36611	1975.924278
36612	1975.924278
36613	1975.924278
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36617	1975.924278
36618	1975.924278
36619	1975.924278
36620	1975.924278

dtype: float64

we print the y to have some information to compare the evaluation of our models to it.

X = df.drop(columns=['Evolution Pop %'])
y = df['Evolution Pop %']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    pipeline.fit(X_train, y_train)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 2.07
Root Mean Squared Error (MSE): 1.44
R-squared (R²): 0.94

=== Support Vector Regressor ===
Mean Squared Error (MSE): 7.90
Root Mean Squared Error (MSE): 2.81
R-squared (R²): 0.77

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 5.56
Root Mean Squared Error (MSE): 2.36
R-squared (R²): 0.84

=== forest random regressor  ===
Mean Squared Error (MSE): 2.37
Root Mean Squared Error (MSE): 1.54
R-squared (R²): 0.93

We retried the same ML models on the same dataset to predict population growth, and here are the results:

• Gradient Boosting Regressor: Errors are very low (RMSE: 1.44), and the variance explained by the model is excellent (R²: 0.94). This model performs exceptionally well, providing highly accurate predictions.

• Support Vector Regressor: The results are weaker, with RMSE: 2.81 and R²: 0.77, indicating that this model struggles compared to the others but still captures some of the patterns.

• K-Nearest Neighbors Regressor: This model achieves moderate results with RMSE: 2.36 and R²: 0.84, showing better performance than Support Vector Regressor but less accuracy than Gradient Boosting and Random Forest.

• Random Forest Regressor: This model performs almost as well as Gradient Boosting, with RMSE: 1.54 and R²: 0.93, making it a strong candidate for accurate predictions.

Calculating the distribuation of the target values so we can understand our results :

# Préparation des données
X = df.drop(columns=['Taux Propriété'])
y = df['Taux Propriété']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Pipeline pour les transformations des colonnes numériques
numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),  # Remplace les valeurs manquantes par la moyenne
    ('scaler', StandardScaler())                 # Normalisation des données
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

# Modèles alternatifs
models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

# Évaluer chaque modèle
for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    # Entraîner le modèle
    pipeline.fit(X_train, y_train)

    # Faire des prédictions
    y_pred = pipeline.predict(X_test)

    # Évaluer les performances
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 28.17
Root Mean Squared Error (MSE): 5.31
R-squared (R²): 0.88

=== Support Vector Regressor ===
Mean Squared Error (MSE): 100.22
Root Mean Squared Error (MSE): 10.01
R-squared (R²): 0.56

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 110.91
Root Mean Squared Error (MSE): 10.53
R-squared (R²): 0.51

=== forest random regressor  ===
Mean Squared Error (MSE): 14.56
Root Mean Squared Error (MSE): 3.82
R-squared (R²): 0.94

We retried the same ML models on the same dataset to predict the ownership rate, and here are the results:

• Gradient Boosting Regressor: Errors are relatively low (RMSE: 5.31), and the variance explained by the model is high (R²: 0.88). This model performs well, providing accurate predictions.

• Support Vector Regressor: The results are poor, with RMSE: 10.01 and R²: 0.56, indicating that this model struggles significantly to make accurate predictions.

• K-Nearest Neighbors Regressor: This model performs the worst, with RMSE: 10.53 and R²: 0.51, suggesting it is unable to capture the underlying data patterns effectively.

• Random Forest Regressor: This model achieves the best results, with RMSE: 3.82 and R²: 0.94, demonstrating excellent predictive capability and highly accurate results.

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dtype: float64

STAGE 3

REGMIXUP

Regmixup is a variant technique of the original mixup sara augmentation method adapted for regression tasks. The goal is to create new training examples that can help the model learn to interpolate between examples and prevent overfitting. How does it work ?

• Shuffle the data ann selec pairs of examples
• Sample the distribution and determine how much influence each of the original paired examples has on the mixed example.
• Creating mixed examples and handeling nan if they appear by replacing them with the mean value of the original data.

Benefits :

• improved robustness (the model stops focusing on specific data points)
• better generelization
• regularization effect (reducing overfitting by mixing examples which augments the data).

import numpy as np
from scipy.stats import beta
import pandas as pd

def regmixup_data(X, y, alpha=0.2):
    """
    Applies RegMixup augmentation to the regression data.

    Args:
        X (pd.DataFrame): Feature data.
        y (pd.Series): Target variable.
        alpha (float): Hyperparameter for the Beta distribution, controlling the degree of mixing.

    Returns:
        pd.DataFrame, pd.Series: Mixed feature data and target variables.
    """
    if alpha > 0:
        lam = beta(alpha, alpha).rvs()
    else:
        lam = 1  # No mixing, direct data

    indices = np.random.permutation(X.index)

    X_mixup = lam * X + (1 - lam) * X.loc[indices].reset_index(drop=True)
    y_mixup = lam * y + (1 - lam) * y.loc[indices].reset_index(drop=True)

    # Check for NaNs and handle if necessary
    if X_mixup.isnull().any().any() or y_mixup.isnull().any():
        X_mixup.fillna(X.mean(), inplace=True)  # Fill NaNs with the mean of original data
        y_mixup.fillna(y.mean(), inplace=True)  # Fill NaNs with the mean of original data

    return X_mixup, y_mixup

X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

X_train_mixup, y_train_mixup = regmixup_data(X_train, y_train)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),
    'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(n_neighbors=5),
    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    pipeline.fit(X_train_mixup, y_train_mixup)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 29977.88
Root Mean Squared Error (MSE): 173.14
R-squared (R²): 0.96

=== Support Vector Regressor ===
Mean Squared Error (MSE): 562506.00
Root Mean Squared Error (MSE): 750.00
R-squared (R²): 0.32

=== K-Nearest Neighbors Regressor ===
Mean Squared Error (MSE): 192318.00
Root Mean Squared Error (MSE): 438.54
R-squared (R²): 0.77

=== forest random regressor  ===
Mean Squared Error (MSE): 36751.35
Root Mean Squared Error (MSE): 191.71
R-squared (R²): 0.96

Comparing result with and without regmixup we can observe :

• The performance of the gradient boosting regression is very similar with and without regmixup.
• The support Vector Regressor shows a noticeable degradation in performance with regmixup. Both MSE and RMSE increased significantly and the R2 decreased indicating less model fit to the data.
• The KNN shows worse performance when using regmixup. It exhibits increases in MSE and RMSE along with a decrease in R2 showing that the regmixup might be detracting from the model's ability to capture the local nuance of the data.
• Similar to Gradient Boosting Regressor, the overall prediction remains similar and robust.

In summary Tree-Based Models handle the introduction of new data through regmixup better than the other models. Non-Tree-Based Models are negatively impacted by regmixup showing worse accuracy.

We should reconsider using regmixup with our models...

Vote proportionnelle en fonction du R^2

X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42),

    'forest random regressor ' : RandomForestRegressor(n_estimators=100, random_state=42)
}

predictions = []
model_weights = []

for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    pipeline.fit(X_train, y_train)

    y_pred = pipeline.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"=== {name} ===")
    print(f"Mean Squared Error (MSE): {mse:.2f}")
    print(f"Root Mean Squared Error (MSE): {np.sqrt(mse):.2f}")
    print(f"R-squared (R²): {r2:.2f}\n")
    predictions.append(y_pred)
    model_weights.append(r2 if r2 > 0 else 0)  # Ignorer les scores R² négatifs

=== Gradient Boosting Regressor ===
Mean Squared Error (MSE): 29051.79
Root Mean Squared Error (MSE): 170.45
R-squared (R²): 0.96

=== forest random regressor  ===
Mean Squared Error (MSE): 29861.43
Root Mean Squared Error (MSE): 172.80
R-squared (R²): 0.96

# Calcul de la prédiction pondérée
predictions = np.array(predictions)
model_weights = np.array(model_weights)

if model_weights.sum() > 0:
    # Normalisation des poids
    normalized_weights = model_weights / model_weights.sum()
    # Prédiction finale pondérée
    final_prediction = np.average(predictions, axis=0, weights=normalized_weights)
else:
    # Si tous les scores R² sont négatifs, on utilise une moyenne simple
    final_prediction = predictions.mean(axis=0)


final_mse = mean_squared_error(y_test, final_prediction)
final_r2 = r2_score(y_test, final_prediction)

print("=== Final Weighted Prediction ===")
print(f"Mean Squared Error (MSE): {final_mse:.2f}")
print(f"Root Mean Squared Error (MSE): {np.sqrt(final_mse):.2f}")
print(f"R-squared (R²): {final_r2:.2f}")

=== Final Weighted Prediction ===
Mean Squared Error (MSE): 27979.88
Root Mean Squared Error (MSE): 167.27
R-squared (R²): 0.97

In this code, proportional voting is used to combine predictions from multiple regression models. Each model provides a prediction, and these predictions are weighted based on their respective performances, measured by the ( R^2 ) score.

This approach allows us to achieve better results with an RMSE of 167.27 and an ( R^2 ) of 0.97. However, the initial results were already very high, making the improvement in prediction performance almost negligible.

Nevertheless, it is worth noting that combining two performant models can further enhance predictions.

Hyperparametres modifications

To refine our approach, we took the decision to try different sets of hyperparametres when defining our models, so that we would determine which hyperparameters have the most impact on our MSE and R² an why.

# Setup code
# Chargement des données
df = pd.read_csv("df_communes_avec_Nan.csv")

# Gestion des valeurs manquantes
numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns
numeric_imputer = SimpleImputer(strategy='mean')
df[numerical_columns] = numeric_imputer.fit_transform(df[numerical_columns])
df = df.drop(['CODGEO', 'LIBGEO'], axis=1)

# Séparation des variables explicatives et cible
X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

# Split des données en ensembles d'entraînement et de test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Prétraitement des données
numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

<ipython-input-4-fa86dacedfcc>:3: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv("df_communes_avec_Nan.csv")

# Here is the code with the original hyperparametres :
models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(
        n_estimators=150, learning_rate=0.05, max_depth=5, subsample=0.8, random_state=42
    ),
    'Support Vector Regressor': SVR(
        kernel='rbf', C=10.0, epsilon=0.2
    ),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(
        n_neighbors=10, weights='distance'
    ),
    'Random Forest Regressor': RandomForestRegressor(
        n_estimators=150, max_depth=10, max_features='sqrt', min_samples_split=5, random_state=42
    )
}

# Training and evaluation
results = []
for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    # Validation croisée pour détecter l'overfitting
    #cv_scores = cross_val_score(pipeline, X_train, y_train, cv=5, scoring='r2')
    pipeline.fit(X_train, y_train)

    # Prédictions sur l'ensemble de test
    y_pred = pipeline.predict(X_test)

    # Calcul des métriques
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    # Stockage des résultats

    results.append({
        'Model': name,
        #'CV R² Mean': np.mean(cv_scores),
        #'CV R² Std': np.std(cv_scores),
        'Test R²': r2,
        'Test MSE': mse
    })

    # Affichage des résultats
    print(f"=== {name} ===")
   # print(f"Cross-Validation R²: {np.mean(cv_scores):.2f} ± {np.std(cv_scores):.2f}")
    print(f"Test R²: {r2:.2f}")
    print(f"Test Mean Squared Error (MSE): {mse:.2f}")
    print(f"Test Root Mean Squared Error (RMSE): {np.sqrt(mse):.2f}\n")

# Résultats sous forme de tableau
results_df = pd.DataFrame(results)
results_df.sort_values(by='Test R²', ascending=False, inplace=True)
print("=== Résultats globaux ===")
print(results_df)

=== Gradient Boosting Regressor ===
Test R²: 0.97
Test Mean Squared Error (MSE): 27483.06
Test Root Mean Squared Error (RMSE): 165.78

=== Support Vector Regressor ===
Test R²: 0.74
Test Mean Squared Error (MSE): 210994.24
Test Root Mean Squared Error (RMSE): 459.34

=== K-Nearest Neighbors Regressor ===
Test R²: 0.79
Test Mean Squared Error (MSE): 175621.55
Test Root Mean Squared Error (RMSE): 419.07

=== Random Forest Regressor ===
Test R²: 0.78
Test Mean Squared Error (MSE): 180424.69
Test Root Mean Squared Error (RMSE): 424.76

=== Résultats globaux ===
                           Model   Test R²       Test MSE
0    Gradient Boosting Regressor  0.966721   27483.062894
2  K-Nearest Neighbors Regressor  0.787341  175621.549355
3        Random Forest Regressor  0.781525  180424.692618
1       Support Vector Regressor  0.744509  210994.238393

And here is the same approach but with different hyperparametres

# Here is the code with the ajusted hyperparameters
models = {
    'Gradient Boosting Regressor': GradientBoostingRegressor(
        n_estimators=300, learning_rate=0.01, max_depth=5, subsample=0.8, random_state=42
    ),
    'Support Vector Regressor': SVR(
        kernel='rbf', C=100.0, epsilon=0.5
    ),
    'K-Nearest Neighbors Regressor': KNeighborsRegressor(
        n_neighbors=5, weights='distance'
    ),
    'Random Forest Regressor': RandomForestRegressor(
        n_estimators=100, max_depth=10, max_features='sqrt', min_samples_split=10, random_state=42
    )
}

# Training and evaluation
results = []
for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    # Validation croisée pour détecter l'overfitting
    #cv_scores = cross_val_score(pipeline, X_train, y_train, cv=5, scoring='r2')
    pipeline.fit(X_train, y_train)

    # Prédictions sur l'ensemble de test
    y_pred = pipeline.predict(X_test)

    # Calcul des métriques
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    # Stockage des résultats

    results.append({
        'Model': name,
        #'CV R² Mean': np.mean(cv_scores),
        #'CV R² Std': np.std(cv_scores),
        'Test R²': r2,
        'Test MSE': mse
    })

    # Affichage des résultats
    print(f"=== {name} ===")
   # print(f"Cross-Validation R²: {np.mean(cv_scores):.2f} ± {np.std(cv_scores):.2f}")
    print(f"Test R²: {r2:.2f}")
    print(f"Test Mean Squared Error (MSE): {mse:.2f}")
    print(f"Test Root Mean Squared Error (RMSE): {np.sqrt(mse):.2f}\n")

# Résultats sous forme de tableau
results_df = pd.DataFrame(results)
results_df.sort_values(by='Test R²', ascending=False, inplace=True)
print("=== Résultats globaux ===")
print(results_df)

=== Gradient Boosting Regressor ===
Test R²: 0.96
Test Mean Squared Error (MSE): 33337.08
Test Root Mean Squared Error (RMSE): 182.58

=== Support Vector Regressor ===
Test R²: 0.85
Test Mean Squared Error (MSE): 120277.50
Test Root Mean Squared Error (RMSE): 346.81

=== K-Nearest Neighbors Regressor ===
Test R²: 0.79
Test Mean Squared Error (MSE): 172488.89
Test Root Mean Squared Error (RMSE): 415.32

=== Random Forest Regressor ===
Test R²: 0.77
Test Mean Squared Error (MSE): 190612.21
Test Root Mean Squared Error (RMSE): 436.59

=== Résultats globaux ===
                           Model   Test R²       Test MSE
0    Gradient Boosting Regressor  0.959632   33337.078743
1       Support Vector Regressor  0.854357  120277.498957
2  K-Nearest Neighbors Regressor  0.791135  172488.890133
3        Random Forest Regressor  0.769189  190612.214968

After the modifications of the hyperparametres, we notice a few things :

• For the Gradient Boosting Regressor, the original hyperparametres indicate that there is a chance we could be overlearning (R²>>0.95). Reducing the learning rate meant that we had to augment the amount of trees by increasing n_estimators, which lead to a slightly more stable R² but a higher MSE. We can't necessarily conclude that these changes are for the better, but they at least have more chances to avoid overlearning.

• For the Support Vector Regressor, augmenting the value of C (the tolerance for errors) from 10 to 100 managed to drastically improve the results, giving a much better R² and MSE than before. The changes to the value of epsilon also had some impact, but it was very slight compared to that of C.

• As for the K-Nearest Neighbors Regressor, reducing the amount of neighbours helped better the margins of R² and MSE, since the data was less smoothed over. Changing the weights from uniform to distance did not seem to impact the results all that much, so we decided to keep distance as the better hyperparameter.

• Finally, for the Random Forest Generator, we instead reduced the amount of trees, and augmented the value of min_samples_split, reducing the complexity of our model. These changes were for the better, although the difference isn't massive and this model remains far behind the others in terms of margins regarding the R² and MSE.

XGBoost

# Séparation des variables explicatives et ciblefrom xgboost import XGBRegressor
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split, cross_val_score


X = df.drop(columns=['Prixm2Moyen'])
y = df['Prixm2Moyen']

# Split des données en ensembles d'entraînement et de test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Prétraitement des données
numeric_features = X.columns
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features)
    ]
)

# Définition des modèles avec des hyperparamètres ajustés
models = {

    'XGBoost Regressor': XGBRegressor(
        n_estimators=200, learning_rate=0.1, max_depth=6, subsample=0.8, colsample_bytree=0.8, random_state=42
    )
}

# Entraînement et évaluation
results = []
for name, model in models.items():
    pipeline = Pipeline(steps=[
        ('preprocessor', preprocessor),
        ('regressor', model)
    ])

    # Validation croisée pour détecter l'overfitting
    cv_scores = cross_val_score(pipeline, X_train, y_train, cv=5, scoring='r2')
    pipeline.fit(X_train, y_train)

    # Prédictions sur l'ensemble de test
    y_pred = pipeline.predict(X_test)

    # Calcul des métriques
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    # Stockage des résultats
    results.append({
        'Model': name,
        'CV R² Mean': np.mean(cv_scores),
        'CV R² Std': np.std(cv_scores),
        'Test R²': r2,
        'Test MSE': mse
    })

    # Affichage des résultats
    print(f"=== {name} ===")
    print(f"Cross-Validation R²: {np.mean(cv_scores):.2f} ± {np.std(cv_scores):.2f}")
    print(f"Test R²: {r2:.2f}")
    print(f"Test Mean Squared Error (MSE): {mse:.2f}")
    print(f"Test Root Mean Squared Error (RMSE): {np.sqrt(mse):.2f}\n")

# Résultats sous forme de tableau
results_df = pd.DataFrame(results)
results_df.sort_values(by='Test R²', ascending=False, inplace=True)
print("=== Résultats globaux ===")
print(results_df)

=== XGBoost Regressor ===
Cross-Validation R²: 0.96 ± 0.01
Test R²: 0.96
Test Mean Squared Error (MSE): 33836.86
Test Root Mean Squared Error (RMSE): 183.95

=== Résultats globaux ===
               Model  CV R² Mean  CV R² Std   Test R²      Test MSE
0  XGBoost Regressor    0.957693   0.007195  0.959027  33836.856251

With XGBoost, we also achieve good results. More explanations are provided in the conclusion.

Conclusion

Stage 1 :

In this stage, our main goal was to create our own database, to try to find links between our indicators and to start applying Machine Learning to it.

First, we gathered the data we needed, cleaned it, and kept the values that mattered for this project before merging the 14 datasets into one.

Our goal was to predict the level/quality of life for each town, specifically how much one inhabitant was making per year on average. We used a dataset that mentioned this indicator, measured in 2014.

We applied several machine learning models to predict this indicator, referred to as "Med14". Unfortunately, the results were not as accurate as we hoped. Even the best-performing models, such as Gradient Boosting Regressor and Random Forest Regressor, only explained a moderate portion of the variance and had relatively high prediction errors. This suggests that either the dataset lacks strong predictive signals for this indicator or that further refinement of the feature engineering and preprocessing steps is necessary.

As Med14 does not provide sufficient predictive power, our next step is to explore different columns in the dataset that we can predict, such as population growth, real estate price per square meter, or ownership rate. By identifying new target variables with stronger correlations to the existing data, we aim to improve model performance and focusn our annalysis on one prediction that will be more reliable and effective.

Stage 2 :

In the second part of our project, we shifted our focus to exploring other target variables to predict, as the Med14 predictions did not provide satisfactory results. We analyzed the performance of various machine learning models on new indicators such as population growth, ownership rate, or even real estate price per square meter.

The results showed significant variation in model performance depending on the target variable. While predictions for population growth and ownership rate yielded good results with models like Random Forest and Gradient Boosting Regressor (achieving R² scores as high as 0.93 and 0.94), the predictions for the real estate price per square meter consistently outperformed the others. This target provided strong predictive power with lower error metrics and high R² values, indicating that it is well-suited for our dataset.

Given these findings, we have decided to focus exclusively on the prediction of the price per square meter in the next phase of our project. This choice will allow us to build more accurate models and derive actionable insights from our data.

Stage 3

In this Stage 3, we explored new methods. The first one, hyperparameter tuning, was interesting to explore, even though it did not yield significantly better results.

Next, the use of RegMixup also provided some interesting insights, but without adding much value overall.

Finally, weighting the R² of the two algorithms resulted in a better RMSE, which, in our case, translates to only a few euros. This is not very significant. These results can be explained by the fact that our initial results were already very good.

Scientific study

Why did we chose XGboost ?

We found a scientific paper : https://arxiv.org/pdf/2402.04082

This study, called an "An Optimal House Price Prediction Algorithm: XGBoost" by Hemlata Sharma, Hitesh Harsora, and Bayode Ogunleye (2024) demonstrates the effectiveness of XGBoost in predicting housing prices. The researchers compared multiple machine learning algorithms, including support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression, using the Ames City housing dataset from Iowa, USA. Their findings indicate that XGBoost outperformed the other models in terms of prediction accuracy.

While this study focuses on data from the United States, its methodology and results are applicable to housing markets in other regions, such as France. The success of XGBoost in capturing complex relationships between housing features and prices suggests it would be a suitable choice for our project on predicting price per square meter in French housing.

Tableau récapitulatif finale